{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一章 OpenCV快速入门"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 python安装opencv\n",
    "目前来说使用的python的版本为3.8版本，可以使用pip list 来看拥有的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                            Version\n",
      "---------------------------------- -------------------\n",
      "-                                  encv-contrib-python\n",
      "-pencv-contrib-python              4.4.0.46\n",
      "-umpy                              1.20.1\n",
      "alabaster                          0.7.12\n",
      "anaconda-client                    1.7.2\n",
      "anaconda-navigator                 2.0.3\n",
      "anaconda-project                   0.9.1\n",
      "anyio                              2.2.0\n",
      "appdirs                            1.4.4\n",
      "argh                               0.26.2\n",
      "argon2-cffi                        20.1.0\n",
      "asn1crypto                         1.4.0\n",
      "astor                              0.8.1\n",
      "astroid                            2.5\n",
      "astropy                            4.2.1\n",
      "async-generator                    1.10\n",
      "atomicwrites                       1.4.0\n",
      "attrs                              20.3.0\n",
      "autopep8                           1.5.6\n",
      "Babel                              2.9.0\n",
      "backcall                           0.2.0\n",
      "backports.functools-lru-cache      1.6.4\n",
      "backports.shutil-get-terminal-size 1.0.0\n",
      "backports.tempfile                 1.0\n",
      "backports.weakref                  1.0.post1\n",
      "bce-python-sdk                     0.8.64\n",
      "bcrypt                             3.2.0\n",
      "beautifulsoup4                     4.9.3\n",
      "bitarray                           1.9.2\n",
      "bkcharts                           0.2\n",
      "black                              19.10b0\n",
      "bleach                             3.3.0\n",
      "bokeh                              2.3.2\n",
      "boto                               2.49.0\n",
      "Bottleneck                         1.3.2\n",
      "brotlipy                           0.7.0\n",
      "cachetools                         5.0.0\n",
      "certifi                            2020.12.5\n",
      "cffi                               1.14.5\n",
      "cfgv                               3.3.1\n",
      "chardet                            4.0.0\n",
      "click                              7.1.2\n",
      "cloudpickle                        1.6.0\n",
      "clyent                             1.2.2\n",
      "colorama                           0.4.4\n",
      "comtypes                           1.1.9\n",
      "conda                              4.10.1\n",
      "conda-build                        3.21.4\n",
      "conda-content-trust                0+unknown\n",
      "conda-package-handling             1.7.3\n",
      "conda-repo-cli                     1.0.4\n",
      "conda-token                        0.3.0\n",
      "conda-verify                       3.4.2\n",
      "contextlib2                        0.6.0.post1\n",
      "cryptography                       3.4.7\n",
      "cssselect                          1.1.0\n",
      "cssutils                           2.4.0\n",
      "cycler                             0.10.0\n",
      "Cython                             0.29.23\n",
      "cytoolz                            0.11.0\n",
      "dask                               2021.4.0\n",
      "decorator                          5.0.6\n",
      "defusedxml                         0.7.1\n",
      "diff-match-patch                   20200713\n",
      "distributed                        2021.4.0\n",
      "docopt                             0.6.2\n",
      "docutils                           0.17\n",
      "entrypoints                        0.3\n",
      "et-xmlfile                         1.0.1\n",
      "fastcache                          1.1.0\n",
      "fasttext                           0.9.1\n",
      "filelock                           3.0.12\n",
      "flake8                             3.9.0\n",
      "Flask                              1.1.2\n",
      "Flask-Babel                        2.0.0\n",
      "fsspec                             0.9.0\n",
      "future                             0.18.2\n",
      "gast                               0.3.3\n",
      "gevent                             21.1.2\n",
      "glob2                              0.7\n",
      "greenlet                           1.0.0\n",
      "h5py                               2.10.0\n",
      "HeapDict                           1.0.1\n",
      "html5lib                           1.1\n",
      "http-client                        0.1.22\n",
      "httpserver                         1.1.0\n",
      "identify                           2.4.12\n",
      "idna                               2.10\n",
      "imagecodecs                        2021.3.31\n",
      "imageio                            2.9.0\n",
      "imagesize                          1.2.0\n",
      "imgaug                             0.4.0\n",
      "importlib-metadata                 3.10.0\n",
      "iniconfig                          1.1.1\n",
      "intervaltree                       3.1.0\n",
      "ipykernel                          5.3.4\n",
      "ipython                            7.22.0\n",
      "ipython-genutils                   0.2.0\n",
      "ipywidgets                         7.6.3\n",
      "isort                              5.8.0\n",
      "itsdangerous                       1.1.0\n",
      "jdcal                              1.4.1\n",
      "jedi                               0.17.2\n",
      "Jinja2                             2.11.3\n",
      "joblib                             1.0.1\n",
      "json5                              0.9.5\n",
      "jsonschema                         3.2.0\n",
      "jupyter                            1.0.0\n",
      "jupyter-client                     6.1.12\n",
      "jupyter-console                    6.4.0\n",
      "jupyter-contrib-core               0.3.3Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "jupyter-contrib-nbextensions       0.5.1\n",
      "jupyter-core                       4.7.1\n",
      "jupyter-highlight-selected-word    0.2.0\n",
      "jupyter-latex-envs                 1.4.6\n",
      "jupyter-nbextensions-configurator  0.4.1\n",
      "jupyter-packaging                  0.7.12\n",
      "jupyter-server                     1.4.1\n",
      "jupyterlab                         3.0.14\n",
      "jupyterlab-pygments                0.1.2\n",
      "jupyterlab-server                  2.4.0\n",
      "jupyterlab-widgets                 1.0.0\n",
      "keyring                            22.3.0\n",
      "kiwisolver                         1.3.1\n",
      "lazy-object-proxy                  1.6.0\n",
      "libarchive-c                       2.9\n",
      "llvmlite                           0.36.0\n",
      "lmdb                               1.3.0\n",
      "locket                             0.2.1\n",
      "lxml                               4.6.3\n",
      "MarkupSafe                         1.1.1\n",
      "matplotlib                         3.3.4\n",
      "mccabe                             0.6.1\n",
      "menuinst                           1.4.16\n",
      "mistune                            0.8.4\n",
      "mkl-fft                            1.3.0\n",
      "mkl-random                         1.2.1\n",
      "mkl-service                        2.3.0\n",
      "mock                               4.0.3\n",
      "more-itertools                     8.7.0\n",
      "mpmath                             1.2.1\n",
      "msgpack                            1.0.2\n",
      "multipledispatch                   0.6.0\n",
      "mypy-extensions                    0.4.3\n",
      "navigator-updater                  0.2.1\n",
      "nbclassic                          0.2.6\n",
      "nbclient                           0.5.3\n",
      "nbconvert                          6.0.7\n",
      "nbformat                           5.1.3\n",
      "nest-asyncio                       1.5.1\n",
      "networkx                           2.5\n",
      "nltk                               3.6.1\n",
      "nodeenv                            1.6.0\n",
      "nose                               1.3.7\n",
      "notebook                           6.3.0\n",
      "numba                              0.53.1\n",
      "numexpr                            2.7.3\n",
      "numpy                              1.19.3\n",
      "numpydoc                           1.1.0\n",
      "olefile                            0.46\n",
      "opencv-contrib-python              4.4.0.46\n",
      "opencv-python                      4.4.0.46\n",
      "openpyxl                           3.0.7\n",
      "packaging                          20.9\n",
      "paddleocr                          2.4.0.3\n",
      "paddlepaddle                       2.0.0rc1\n",
      "pandas                             1.2.4\n",
      "pandocfilters                      1.4.3\n",
      "paramiko                           2.7.2\n",
      "parso                              0.7.0\n",
      "partd                              1.2.0\n",
      "path                               15.1.2\n",
      "pathlib2                           2.3.5\n",
      "pathspec                           0.7.0\n",
      "patsy                              0.5.1\n",
      "pep8                               1.7.1\n",
      "pexpect                            4.8.0\n",
      "pickleshare                        0.7.5\n",
      "Pillow                             8.2.0\n",
      "pip                                21.0.1\n",
      "pkginfo                            1.7.0\n",
      "platformdirs                       2.4.0\n",
      "pluggy                             0.13.1\n",
      "ply                                3.11\n",
      "pre-commit                         2.17.0\n",
      "premailer                          3.10.0\n",
      "prometheus-client                  0.10.1\n",
      "prompt-toolkit                     3.0.17\n",
      "protobuf                           3.19.4\n",
      "psutil                             5.8.0\n",
      "ptyprocess                         0.7.0\n",
      "py                                 1.10.0\n",
      "pybind11                           2.9.2\n",
      "pyclipper                          1.3.0.post2\n",
      "pycodestyle                        2.7.0\n",
      "pycosat                            0.6.3\n",
      "pycparser                          2.20\n",
      "pycryptodome                       3.14.1\n",
      "pycurl                             7.43.0.6\n",
      "pydocstyle                         6.0.0\n",
      "pyerfa                             1.7.3\n",
      "pyflakes                           2.3.1\n",
      "Pygments                           2.8.1\n",
      "pylint                             2.7.4\n",
      "pyls-black                         0.4.6\n",
      "pyls-spyder                        0.3.2\n",
      "PyNaCl                             1.4.0\n",
      "pyodbc                             4.0.0-unsupported\n",
      "pyOpenSSL                          20.0.1\n",
      "pyparsing                          2.4.7\n",
      "PyQt5                              5.15.6\n",
      "PyQt5-Qt5                          5.15.2\n",
      "PyQt5-sip                          12.9.1\n",
      "pyreadline                         2.1\n",
      "pyrsistent                         0.17.3\n",
      "PySocks                            1.7.1\n",
      "pytest                             6.2.3\n",
      "python-dateutil                    2.8.1\n",
      "python-jsonrpc-server              0.4.0\n",
      "python-language-server             0.36.2\n",
      "python-Levenshtein                 0.12.2\n",
      "pytz                               2021.1\n",
      "PyWavelets                         1.1.1\n",
      "pywin32                            227\n",
      "pywin32-ctypes                     0.2.0\n",
      "pywinpty                           0.5.7\n",
      "PyYAML                             5.4.1\n",
      "pyzmq                              20.0.0\n",
      "QDarkStyle                         2.8.1\n",
      "QtAwesome                          1.0.2\n",
      "qtconsole                          5.0.3\n",
      "QtPy                               1.9.0\n",
      "regex                              2021.4.4\n",
      "requests                           2.25.1\n",
      "rope                               0.18.0\n",
      "Rtree                              0.9.7\n",
      "ruamel-yaml-conda                  0.15.100\n",
      "scikit-image                       0.18.1\n",
      "scikit-learn                       0.24.1\n",
      "scipy                              1.6.2\n",
      "seaborn                            0.11.1\n",
      "Send2Trash                         1.5.0\n",
      "setuptools                         52.0.0.post20210125\n",
      "Shapely                            1.8.1.post1\n",
      "shellcheck-py                      0.8.0.4\n",
      "simplegeneric                      0.8.1\n",
      "singledispatch                     0.0.0\n",
      "sip                                4.19.13\n",
      "six                                1.15.0\n",
      "smop                               0.41\n",
      "sniffio                            1.2.0\n",
      "snowballstemmer                    2.1.0\n",
      "some-package                       0.1\n",
      "sortedcollections                  2.1.0\n",
      "sortedcontainers                   2.3.0\n",
      "soupsieve                          2.2.1\n",
      "Sphinx                             4.0.1\n",
      "sphinxcontrib-applehelp            1.0.2\n",
      "sphinxcontrib-devhelp              1.0.2\n",
      "sphinxcontrib-htmlhelp             1.0.3\n",
      "sphinxcontrib-jsmath               1.0.1\n",
      "sphinxcontrib-qthelp               1.0.3\n",
      "sphinxcontrib-serializinghtml      1.1.4\n",
      "sphinxcontrib-websupport           1.2.4\n",
      "spyder                             4.2.5\n",
      "spyder-kernels                     1.10.2\n",
      "SQLAlchemy                         1.4.7\n",
      "statsmodels                        0.12.2\n",
      "sympy                              1.8\n",
      "tables                             3.6.1\n",
      "tblib                              1.7.0\n",
      "terminado                          0.9.4\n",
      "testpath                           0.4.4\n",
      "textdistance                       4.2.1\n",
      "threadpoolctl                      2.1.0\n",
      "three-merge                        0.1.1\n",
      "tifffile                           2021.4.8\n",
      "toml                               0.10.2\n",
      "toolz                              0.11.1\n",
      "tornado                            6.1\n",
      "tqdm                               4.59.0\n",
      "traitlets                          5.0.5\n",
      "typed-ast                          1.4.2\n",
      "typing-extensions                  3.7.4.3\n",
      "ujson                              4.0.2\n",
      "unicodecsv                         0.14.1\n",
      "urllib3                            1.26.4\n",
      "visualdl                           2.2.3\n",
      "watchdog                           1.0.2\n",
      "wcwidth                            0.2.5\n",
      "webencodings                       0.5.1\n",
      "Werkzeug                           1.0.1\n",
      "wheel                              0.36.2\n",
      "widgetsnbextension                 3.5.1\n",
      "win-inet-pton                      1.1.0\n",
      "win-unicode-console                0.5\n",
      "wincertstore                       0.2\n",
      "wrapt                              1.12.1\n",
      "xlrd                               2.0.1\n",
      "XlsxWriter                         1.3.8\n",
      "xlwings                            0.23.0\n",
      "xlwt                               1.3.0\n",
      "xmltodict                          0.12.0\n",
      "yapf                               0.31.0\n",
      "zict                               2.0.0\n",
      "zipp                               3.4.1\n",
      "zope.event                         4.5.0\n",
      "zope.interface                     5.3.0\n"
     ]
    }
   ],
   "source": [
    "pip list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到里面是有opencv的库，使用的opencv-4.4.0.26   \n",
    "在命令行当中使用pip install opencv-python 就可以安装python 当中的opencv的库了   \n",
    "在没有使用换源的情况下，下载的速度是很慢的，所以可以使用下面这个命令来进行安装  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "pip install opencv-python==4.4.0.46 -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "解释一下该句，==后面是指定对应的版本 ，因为我使用的4.4.0.46 所以我在这里给出的就是4.4.0.46  \n",
    "而-i的意思是指定安装路径，这里可以是你本地路径，也可以是一个网址路径，我在这里使用的是清华的镜像源  \n",
    "为了避免使用的库在低版本拥有而高版本没有了，在这个地方在安装一个对应的 opencv-contrib-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "powershell"
    }
   },
   "outputs": [],
   "source": [
    "pip install opencv-contrib-python==4.4.0.46 -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当上述的两个代码执行完之后，可以再通过pip list 进行查看是否安装了这两个的库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 图像的基本操作\n",
    "### 1.2.1读取图像\n",
    "使用img = cv2.imread(fileName,flags)  来读图像\n",
    "其中:\n",
    "* img 是返回值，其值是读取到的图像，如果未读取到图像则返回\"None\"\n",
    "* fileName 表示的是要读取的图像的完整文件名\n",
    "* flags 是读取标记，默认值为1，不填写即为默认，其对应的含义如下表： \n",
    "\n",
    "|  对应的值 |   对应的含义   | 数字代替值 |\n",
    "|:----------:|:-------------:|:----------:|\n",
    "|cv2.IMREAD_UNCHANGED|保持原本格式不变换|-1|\n",
    "|cv2.IMREAD_GRAYSCALE|将图像调整为单通道的灰度图|0|\n",
    "|cv2.IMREAD_COLOR|将图像调整为BGR的3通道图像。该值为默认值|1|\n",
    "|cv2.IMREAD_ANYDEPTH|当图像深度为16或者32的时候，返回对应的深度图像，不然就转化为对应的8位深度图像|2|\n",
    "|cv2.IMREAD_ANYCOLOR|以任何一种的颜色格式读取图像|4|\n",
    "|cv2.IMREAD_LOAD_GDAL|使用gdal驱动程序加载图像|8|\n",
    "|cv2.IMREAD_REDUCED_GRAYSCALE_N|将图像转化为单通道的灰度图并且使图像的尺寸减小1/N,N可以取2、4、8| |\n",
    "|cv2.IMREAD_REDUCED_COLOR_N|将图像转化为BGR彩色图像并且使图像的尺寸减小为原来的1/N,N可以取2、4、8| |\n",
    "|cv2.IMREAD_IGNORE_ORIENTATION|不以EXIF的方向位标记旋转图像| |  \n",
    "\n",
    "要注意到imread当中支持了绝大多数的图像格式：  \n",
    ".bmp、.dib、.jpeg、.jpg、.jpe、.jp2、.png、.webp、.pbm、.pgm、.pxm、.pnm等等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cd5038c910>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 例如我们读取一个图像并且显示出来\n",
    "import cv2      # 这一句是必须要包含的,引入opencv的库\n",
    "from  matplotlib import pyplot as plt\n",
    "\n",
    "img = cv2.imread(\"../img_use/kauile.jpg\")\n",
    "cv2.imshow(\"test\",img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "img2 = img[:,:,::-1]  # 转化图像到RGB\n",
    "plt.imshow(img2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可能在这个地方很多的人会迷惑  \n",
    "cv2.imshow是干什么的，waitkey是干什么的？destroyAllWindows是干什么的？  \n",
    "后面那两句又是干什么的？  \n",
    "目前来看是不需要理解的，运行上面的那个代码的时候会发现会展示出来一个图片并且等你按下一个按键之后就消失不见了，而且在jupyter的界面当中显示出来了你刚刚的图片  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在上述代码当中，如果想要代码正确的运行，需要使用`import cv2`进行导入opencv的模块，并且在使用opencv的函数的时候，需要在前面添加`cv2.`作为前缀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[27 27 27]\n",
      "  [27 27 27]\n",
      "  [27 27 27]\n",
      "  ...\n",
      "  [59 56 65]\n",
      "  [58 55 64]\n",
      "  [57 54 63]]\n",
      "\n",
      " [[27 27 27]\n",
      "  [27 27 27]\n",
      "  [27 27 27]\n",
      "  ...\n",
      "  [58 55 64]\n",
      "  [56 53 62]\n",
      "  [55 52 61]]\n",
      "\n",
      " [[27 27 27]\n",
      "  [27 27 27]\n",
      "  [27 27 27]\n",
      "  ...\n",
      "  [55 52 61]\n",
      "  [54 51 60]\n",
      "  [53 50 59]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[27 30 35]\n",
      "  [27 30 35]\n",
      "  [28 31 36]\n",
      "  ...\n",
      "  [58 57 66]\n",
      "  [69 64 73]\n",
      "  [69 64 73]]\n",
      "\n",
      " [[27 30 35]\n",
      "  [27 30 35]\n",
      "  [28 31 36]\n",
      "  ...\n",
      "  [61 60 69]\n",
      "  [73 68 77]\n",
      "  [73 68 77]]\n",
      "\n",
      " [[27 30 35]\n",
      "  [27 30 35]\n",
      "  [28 31 36]\n",
      "  ...\n",
      "  [63 62 71]\n",
      "  [76 71 80]\n",
      "  [77 72 81]]]\n"
     ]
    }
   ],
   "source": [
    "# 因为前面的代码块当中已经有过import cv2了，所以在这里就不需要再导入一遍了\n",
    "img = cv2.imread(\"../img_use/kauile.jpg\")\n",
    "print(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述代码当中，使用python的自带函数`print`输出了一个图像，可以看到其中是一个一个的矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.2 显示图像\n",
    "在OpenCV当中提供了很多的与显示有关的函数   \n",
    "#### 1.nameWindows函数\n",
    "(该函数在OpenCV4版本当中的部分功能已经被合并到imshow函数当中了)  \n",
    "该函数的意义是用来创建指定名称的窗口，其语法格式如下：  \n",
    "```\n",
    "None = cv2.nameWindow(windowName)\n",
    "```\n",
    "其中的windowName指的是窗口的名字，例如下面的代码语句就是创建一个名字叫做Img_show的窗体：  \n",
    "`cv2.nameWindow(\"Img_show\")`\n",
    "#### 2.inshow函数\n",
    "imshow函数的语法格式:  \n",
    "```\n",
    "None = cv2.inshow(windowName,Mat)\n",
    "```\n",
    "其中有两个参数，第一个是窗体的名字，第二个是传入的Mat类型的图片数据  \n",
    "在OpenCV4的版本当中并不需要创建一个窗体再去显示了，inshow这个函数会自动检测是否有这个窗体，如果没有就新建一个这个名称的窗体并且在这个窗体上显示图片  \n",
    "* 注意：在jupyter的文件当中使用inshow还在显示出来一个窗体，所以更简易使用：\n",
    "```\n",
    "img2 = img[:,:,::-1]  # 转化图像到RGB\n",
    "plt.imshow(img2)\n",
    "```\n",
    "进行显示图像，因为使用imread函数会把图像转化为对应的bgr格式，而使用plt.imshow是显示rgb格式，所以要对应进行转换  \n",
    "在更多的时候，可以自行定义一个函数用于显示图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imgshow(img):\n",
    "    img2 = img[:,:,::-1]  # 转化图像到RGB\n",
    "    plt.imshow(img2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "例如使用下面的一段程序进行显示一个图片到Window窗体和在jupyter上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ArsZBQF210xhXXd/ZhZs9CGNE40ajZCknhpiEmAsJwBqc87gijDGSYyajhiUXKCekp1SON8mJQYpUtxCMMzTe0bUd282GFAP29oYpHBhjpOCw1uNtpHGzu11o6o0m60kgCKZ6TSlnJMbl1Dj+UwTeGqHxjidPPub58+eIMXzzvfe429/y5MnHHHZ7bm9vefHiBTc31zx/8ZRh6Be8Yp63mbADp1yz8toU2OfNzX/2vf2M35+GJoiGcgrz1ayTABYxhoDjH333B/zn/+V/xV/5S/8ch4OGSl3XsulW5JS4OxyIYtiPPU+eP6cYS7s5Z9U6SAVKJoWALRnnDU1jMNnReoOdM2EUhphIZSZ2VeAwzfOX6nosxwySHlmLx3UKmp+OUzzhHrfn9PdzWj0l3eSwpMudddjOEEJgGBVPElONlRicdxjn7v0N59xX20AgouGAZKIBSAu48+oS0+CrSD7+WAGkAtXiK1ZsQdFtCg7wRlg1LVMauR01XSQnt1NqHt/ESElZkeAsigAXo+5+UqahBcIUGIY9u7sdt7e3jMkgriWGiZw0ni0V12i9Em6on7HUy5qBqPkEmN3LJTsRIgbDYXfgww8+xBjh/PycP/qjP+QP/+iPSVNi1a5ou4ZhGHn+/Iq73S3Agm+EEOrc3T+9pMYUM/tPw4yfmu/whcbRZykv/6KGOOrOpxD57d/57xkOA29frLAhcfHgnO35Q7CG0vfc7G65vr3i0O8pWKapx5otKWWGaaLNmRwj+AJF8Aa8tfimpWk6xgKNz4SYiaWo11iAopiSejygvpZZPnWh1HVzvL+nBuDllOXpY/P3kmuiub4up4FYQ1DnHNaExUOYWcBlNk1GkBMMYx4zwP2m8aU3EDOp45QpeUwVyXLaLTlijqDX6esWwEdErWpW3IHZ3ZLCqvWMscHdWX3/GpJIoVpoi9S8vXpsx5tpjNETKCXGceL25pb94Y79fk8IgZAMUmx1coSmbSiN0nPbtkOMIeVESkfi0AxEnQK08/UoKJW5vb3hd37ndwhh5L1vvsf3v/99nr3QMGNMI1MIHPo9H3zwAX1/wPtGZ+hkUbwOAL7/e70Tn7aQfh7G4/gnFrYLFKFkYegTvtVr++M//R5PNy2Pth2bbsXDylodY6HvJ8ZRN1KIAVMym3WHLQM5K7U8HXo611Xg0WGNxVmPMUqHNxiMLVhNryClKC4yH1ymYgLlCCDrp9aLkBMDcbw2eWUOT42Hfp8rqe0I2s8cE00LoxwHWMDL+T0yLBmN0zGHI28aX34DcYLe51LIy0TK8vv7X1lWkszGAHWlvKsZAZTTb1JYTunGtjgROu9pvSdOY3UJhZIz3loa3zBOE1Cps84tCLS1Fod6GTlnDof9YhxKzuQMktPCfrTWYpuW9WqFc5YwTZreq/iIzKHEiXE7NRLjODD2e/7gD/6Avu95+OgBT58+JaXEb/7Gb/Di6orf/e3f5cXzK6YwLjgGpCXleZo+flOG6Ke5718+LX/iUdHSnAGxhKSna0iRZ1GBRW8dtvF0Xcf1jXoNje+w1hBjYNu1PHpwQRkN+8Md4zTiTMMa8NZhxCKlrhexDP3ImLN6B0aB7hwrwGz086i3OnufdS0WDVGORkJe4ba8vHnnNObxOboeysk8zngEcD+jkXN1HOzy3BjD8nmWKfyMe/GlNxDAsbjHGMhpOdHmcc/6yn2jMaf+lEarBU+megTGiBJZKsHIVmTYOw9hrFCGngYGUSPhHN5ajHVY76oBqFRi4RjTSb2BSV29nAqFyJyAyTnjjcE6R0qZaZowztNt1pyfn7NZr5XrXwlToO83G6AQIt/97p/ywx/+EO89z58949HjR7z77rt88NGH/M7f/x2effKcFDMi5WhkTkKF1xOWfkqb9+c5KofluCkMfRhxDp7c3OA+8jw6P2MKI3iLb1w1HPDowTkPLrbcXQ+EQ8JbwTgNGUtJGBosghTFe8iJFCcwFmdbTJmLzU48AziSG+CY8akZLuRYtHY63y+f5KcUedBCQqnpbU4Ojvl+hhh0PRo9aDRdXJZMUylgKIuBAZZ0+ZvGV8JALKFC/f7UxYL78dtpeKG/m9O/CkSkrHG7qfx9KQXDsZDJyKsEFVAmYE4JRNSAzFWb1t4Lc9R9B1+r+3IFA1M2ZBIZQYxgjMU3Dd45ZkBrs9lw8fAhl5eXNG1L49VAuAoszRTdUgq3t7c8efKEw+HAMAy8++7bWGv5/d//ff7h7/9Ddvs9ecoIBuvtUpsRashyOm8vz/X98dkG44uGFz/NcKQgFMkUiYjR1LJRRJFQMrFkbvcHiJkpDpw/uqRpOs7Pz7ACDy8vaFvHrWjKu21bVus1xhpSTEBQenIKtM5ytl4hI4SUFbhEMYVUCuTj+jOn1yjH035eh/PPy3W8IWvx0iMUY5Y7ckqznz1s5A1VvWX53/35e4Vzcn98JQzEMuaNLmbBIOB0ck+y6ot3d0o6qdFr0dy6M+queWeXUlgvRot19rK8FjRWCzFqGrW6eS/HkBoOKAVXU5UNbdPAvtfFVku6rVFEedV1tG23GIGu67g4P+fy8lKBy/pcU70GOBJkPvjgfUKMeK+1BcZafvd3f5cnT55we3tLLoVVt8aKJZd0Ui14/DfP1/3F+cU9iNNY9+c5Sj0QMoViMtTMzgwujykx5sQUI/s8cLe/RZqGtx494J133qJ1lq5xDEMPVis0N5sNZxfnFNtSSuFuf8f19QvIkbPtCswDmr1jdxgQYxWfyoUQMgE4pfkXNPNWMctXxmIs6jqzlZMzjzlbNnsMKSnrcQEpc75nIMoc3ty7x/q37/35+dTkdUbo/vhKGIjjAtba+1IEwR69BY5VmwKUYoGMpeCMxRnBSMYLdNbiBIqDxhtiTngHq85xtu1IBp7fOHzWGoCsb6hchqxch5VrmOqpZSUhaCm4WIcYAclaS9G0WgFaRJdxLuAMYh3Ot1jb0DQtbduwXm25PLvkfL1l7Vsl2FScYK7ktNYyDAM3Nzd8/PHH7O9uePBAqz3/+I/+kKdPP2G/u4WSaKzFe8gpkFJYcAeR/Irrepznn/29/FkNKUCuPJGslaAgxCAMMeNazW7tbq95751HPLp4hORAHPZcPx0JRkjSkMVhjcc4Tylwc3fD9c1zcg6sGgd0mh1I6saLZFbeMk6ZPhVNf1aDuYDJVALdy0b0NOdZU5HLqAB6MWZJpWI1EzITzOa0uz6/vk39m8e3UQuVZ2M1g5x8Pk/uS28gFtCllHsr+N5illkA42gVS9aT3jtHY52WeJeMlULrHcUJEQ9TqRu0Y7NekSqQ5IzVe1Tdw1Q/Q+M84htKDsQSKVK5AqVgHBjuZ1amadJMBEY5DaUsIYZzDW2rZd1nZ2dsNhu6tlUPo8anp5mYUgr7/Z6PPvqIu7s7zjZrVl3Hn/7pn/Lkycfc7e60EKl6JGPNhc/MU53C+4U+/0SNetpqdioTc6F0BmM9WIuxnnEYICRMgn63J1phtd5i1iuapoViCCFBniglcX19xe6wYwwjWMMwjaSclAXLsSK427RsjSOmY7g6k5VOf56/nzMPp0MdiKNXbM0JtX7G0+pmL1RvshrFU4ByBp9PvYhcZixCjc1pdu/TxpfeQMBr4rPXxlMvpQIrmciKwRmruWxj8EZDCtMagnEUSThv6PueYbXCVIaibzx5EmKOtTy2LO+VREErkVxZm7XmvwApkyqbbZomwsyXX9T9jjdm1a05P7/g/Pyc7faM1Wq1aD/MN+70JLq5ueH58+c8efKEzXqDs5kf/ehHfO9736MfDsQYFqMSY6wpsMSRvAM/icrgl92omAomShHAaPYoQYiF1Ai+7TiMAze3O7wVdjc3rDrPxYPHbB88JGOw1pNSJoSBw2Hi2bPn3N7eEkoiR9gfDuzHiWFKxFygRLwRmu4M47vlIJkNwOnGPa2duBcC1PGmNOeczbIVUEwc98RsDE4reE/HKRlOypHYNeNnX32i1OmYrSu8xh++n9cXqQZingjrsDU16axFrMFmlScTRKswDwN+DatVR9M4Yo7ESWXKMGCdwXlDGEvNeZfFhZtBVGW7aY3ENAZSrKeIqOcwb3hrDE3bcnZ2Vg3EltVqtZR2Cywod8qZcRx58eIFu92OrusQgQ8/+B7f+9M/Zb/fK+J+Ut2p6b84z8hL03j8+cfJWnzZDIWcfq1pboshmcKYIoexx/pCY4WxwJPrK8RBrmX12VhELGEKxJix1oNoOLfb3TGFieIsU0rsw8Q+BMYQa6gRkZKQIYG4e/N5n616nOuXy7WX6zjFE04MyFK9Cwq0L/EExz1Ryms9gjk8zRRl6lZa/Wwgmqo38qbxlTAQr1rcl0GYlwgpdbPOlleqKziTnVTpZ+ZS6M1om4bGezabDe+tGm4Pe0IKpGw1e1FU5MU3juAtK9vixJFjIk1KaW2cY9W1VVINwhQJU9VBWDjzZWHWnSoAzUVZc9pJAGsMoRZR3d7eqpczDFxcXPCd73zCD37wA25ubpSebeVEmSkt83GK3/w078eXyUhULHqxEBqfa1o8okbCThOjwJAyN/3Aph9ZO08Sy74faQ690uurnqVrGnIuTGEkl0wpQsiZiJARUinEXGqBmVDqGjmdm5dB7JfJbkdtzlcNxWlIcg+Mz5oznYVtZlbm7EWcvt+9v1E93VyO779gFZ9yRnzpDcTpROUTo1DK0XAgJ9JypWiBjBSMc1gx5BiRorX6626FFTiEYVEx0q86nHO0Ylh1HbvDDpscjBOpip36xrNer2lKIkki1oU3q0CtVy0XF+fkDNdyCyhFujhHMZaQC8boZzyt7YdXdS9mtzTGyOFwWEhdxhhubm45HA7Lvo8xLYKsFcL6qW/iL5thgPs43+mYS/tDipTJKAFNCojjMCau7g6kVYexntvdnvV2xWq9oVutaduWlCGlyDhOCx051iI8axxGsoLTs1HKpiYH7mfWTk/1U87CqSzgaRj5snE5DU9SCIjVx+dQZjH/J+/ByWPLPZuxtZck6Wbw+k3jS28gFN2t35aa4nyNzNkbRwWsSkw4MazajpIjaUxLqtTYeaPqRuxTYFpYZxkxkHOsMT5Y646AIxWhzgXvbQWSMm3b4rxmNZz3JDEkatXfXOKbEiFMxNhUtaZESqpRqOIxgRAT/dAzTuMi2jIMAx999JFqN4hRWOGEdffyyfXzGl+Uiv26Z3/xTyvLl3L8iTR7mTkTKYQ8YgW8NSpWvDuwXW8IRbi6vePhw0u2Z65WN1qGOs/WGlbdimSNEuqCKpYuDN9c1whyrxrzyJE5EppO62iAVzyEl0fXdepVWqO4yDgyjSpzqHCXqlWdbvjXMWM1narSBNb5e+xcpQ28eXa//AYCoGYG9EILdUfce8ZJarc+MJ8uFTVOymGw1pBERUJM1HRoySqkmseAHwf6GBinqao+ZVRgRmP7aRy1aCYlclGNv5zUUDjrmaXOjdGSYyrNm2Ip4mqBWAWLpJCqQImInlgxSv2+8uRz4bA/LEpQH334Idc3Nzz95BN1axc23ulC+2xc4fVpzs+PR7xuYb+Zifl5t32p0NJ8ir72KSfvWjeB1NT3ibud6+OIVmDmlEgimoHIkWYyZFG9y0M/MsVM02oB2ziO3N7tuL5RPknTtGRjCWXCECgx46zDO4PMqdWiSlin8zNbrFlJaj6t51DwmHo+Xu/xewVZZbNm025ZbxrIG6Zh5HA4MIbAMI2LhODLh8MrRidXjyoGDPcNxFfbg0AwMqf8NHSYc7hHSKIshkBEtCJc0DLtWay2VDUpa/He4EZHGVVDUCSRipBjxOVASIVxVMCqJCBrSXeaAnEc9Hsq3z3lhWc/hwgxJKxNpFhwrsVbTx+BYjARrPE0TVe9hHiv1mIOqWKM9H1PSokXz59y9eIFN1cvePrkY549f0ZJATFlkU2/f35+sfF5DcPLi+70dacLU2o2oT6gG6ecBAPzc5ey/OVdmCnBcJRf4+R5is9IveccH5vf/qUpOLr186mttPm7IfLDJ885WzesG8fVvucbCNI0hGHi6vaOF1fX9OOkhh9wCDYDoRLPjEUknfzRI4sxZz081AO2ilvUtOfLPBT9jBxp0ai3PA5K656GA03T0FUF9lXbLdftvT+qVp9kSE7v0cvhSkYwM4ZRvaw3jS+9gdDCqqZKjkWgVHT/VAC1kJfeDEK20DiHtB7xFmJUl76y7rqmUaAnJiRDkUKiEBKYrB6Kpseyrs2swiAlaVhiq7DsfGOstbRtq2rCTaOlw8MIGLpuRbveYobEXT/hvGG9PePBg4e0bXsPLJpvbj/0DP1ACBNPnjzhj//oj3j2/Bl933Nzfc3d3R0xjhSmk1P2NBp/NYX2pvHy8+by4KMRvg+sve51GhIpt2Nm40ievTpBcPc37vI+KkhzKqOyXEE+QecNCwCpZfizFgOVF3BiZspsRGajdXJClrIcLFOG6/3E3RjZtI7mo2e05xc86gu73Z6PPn7Ki5tbhn6kbVVhu+RCYwQrkTgd1PBVKTcViTGLiZgNgX6kROFUC1JneOYmiDHVKp54AlSFqZRUXl+E1nvVvBS7cCFmrZJT7sPpOM1QaB1Iqbdo5hZ9ur/55TcQJ/GVKVY9CCmcHmb3LlDRS4opWoxVMqkkUomMYWC3vyXlFSElSoISM6FU8Y22xYpTARnvYRhJ6nZgQHto+GaRFz/NrsxFVb5KjMVUVApffUzEaAzknGWzXnN5ccl6u72nMTjHpFNU5e3nz57yO3//t/ne977Hfr8nxlC7NpW6GU9PiC+erjzx5k/msW7MmgmadTGM3I9ZjZjlXthqIMRYxVdSUmOc6/Pmc3957Rwc6M7POVYPUABzFPJZPtfxk+W6xhVzeFP8LPfZHqeboNTPYhSnCCGS0sSffv+H7PZ7Ls4uKCmz3x8YaziZyoQtWt1pjaF1DaOdVNE8R4pYskDBHSsts1DK7OW+JgQUjmQ4U/ELMXW+6zKeU5vV1xrGwCQT+YTzkIQT8H42jSczcYpHFbBGKKZgxeJmwPRTcKqvjIHQCTEni+tTxr2c8Jxz1pThYegJKXK73zGEEVNkARpjSkxhIsqsFXFyUgqcn52z3W64ub5ZhFxmNHomOI3jSB5GclHyVT/0FOsYI6SSsb5hc7Zle35G07X3kOs5/k0p8eTJx/z2b/82f/zHf0zf97VQq1BKohTdkD/JmM9sNRJL8H40AkW3r7EGixpbEcFV9e0Z27LCiYEwuqFiIjItacMiVsVuy2wk3WyGyBhihiylbiih5ACz9yJHY7F4GkePXj2JwlHP8qVpOZ7I9f8nOEUpsxguDDny0ZMX3Fzv8dYfjVLWdKZHD4h15Q20bUs/jYzTwBQSMcPcvE4QxBrEuGVezYwv1HjoFKich4qmVWNfPaZcjhs4ko+g+8n6PDUQ88Ght/U+vqB0dBADzju6pmHVdXz45JM3rpMvvYEoJ4t2TinpOKUg33+NsZa2aejaDnKgJF0IUwzsDge899okpdQbkXThTjHCMJCAqRxdRETTQxeXF1xcXLC7vVNW3MmpMJNPQggc+p4xZK20HEfwDaFYihjarqHbbPBtu7xOROPT3W7HOI786P0f8Y++8yd85zvf4XDYs/TsFL25utHm5ievhgmfN3OxIBfliOvUN8SIwSK03uHlyCmZU3OlqJhO62qti9HFLwWyTQTRfqTGOJKZPQgWNF//Tm1AlIQkBaqBIGVCyZSs+YHCsZXBcqUVayqU46kLfPbpcX+OclZwMWOZpkxJE85o96zGOwxCTKoW5Y2lkY6zzYbHjx+DwDD07A4DwzgRT/64Hh61dgiYZedSKaSihuFwOCyfQ8NVXYclz+HAiVdXZuX1Kso82xqOBL2Xw8DTn0u9x5QCWTUsskm1NOAr7kHMX0VqxVrJdfLn3oeFnO+LcXZOG84QIdmIWC16CSEwhIlk9ERHau0/gjeOKUWmlNiFiZRUjs4Zw6pWZq43ax4+esQ0ThyGA6AciGmamCnOMURCSAswOk0TWRqs97imQYwwjANIWURDD4cDH374IU+ePOHJ00/o+wMhjFVVunLtUW2HWWj2ZUzgi6Y0BargjcbBRgSSegvOWFrrcSbhjaFxGv+KqTUBVSO0dQ4nSkOPMeKMwTYeaTvm+N96t5xss6EbhpFUNBRpcqv0YQEwNAh9mAgxMcWEiCVWhaactA6CkxCoHoxfHKItem/BkDDkAjkWilNSXKwl3cZorD+OI6Hqh5RSWK83nG23XJyfV0nCeX0e6e7jODJOI/0Y6aPhrp/xpZopq+X3Oc8Nn8riaQlQ+wxQzFFftZjqvVXD8TL3YV4PpyNTPcL6u5gj41SwvX3luafjS28g4GgcZlGXdOI1zO7w6TDUGgxrETzGRxprcY2HGMkp1qZxWiFRBO2DUdOoIaaTNJXebFd1HxrvOTs7Y3o4IDe6AeZmtCEE5SqECbC0XUsomZATvnPgnEqdhcA4DIiVpWbjcDgs4cp7732Tjz/+cElhaU59Tpu+agx+HLo0sJy8IkpNdhisExrnabynEYc3kcaaKpTjjiCgqQbCN1hRMZUo9XnOMRuHQqHpGrq2PQEnYe8UU0lABKaciVm9hrYztNYSUmKcIn3OTLUwKZS5GOvoTczR9/z1C82B1LCGOYTJs04QJevaMCIYrMbvZHZDz9XdLdYYupUyZ1frNW3jFAav92MYBvb7PYe+px8zY7IMcSKGON84loVcClRqvJRZyEVYVMRLUY+pQEk6rynPREF5xVu4f333VT0r/kvMmcPQf7X7YgCvpG/edD1HF7m6zaUcc8v1X67GIBTtZyAVJAwxLO52Sidin8uba9YjF82itE27lF9rLUThqBCksWDbWPoYiSnTdB3Gd5gqT78/HDBWuL6+Zrfb8ejRI7797W+z3+/5/g9+wIvnzysmoCkz533N4nz2eBm0XWL2k2Uyx+y2Iu/eGLxYnDG01rNyDa3xNNbR2mo0KjPVG7MU/jhnESkYZ8kxqSEtutlmnQvnNbsxhxelFFpKbV5b6GMkZG2WXIoQTKIzwhQzo0RMTDgTiUUNxRhDzUjNRqHcu+py71pPbuEbcArQjlRzK4XE0TNRZ0k3X2MbijOLSE8pIHd3mLl3SWU55nrfYkzaSTwXchJiFlKJS8ZD70OpbRiUkyNoy7+5Z6szFitCllxB70LJKF6W4rEu4+TCXvEkZ4ymlKUT3CKMXAvK3jS+EgbiGOdbilF6ayn3SR73qaoax5WoSkA5Ju2gJYZsLAWrfRZTAolKzU4qPSIoQ+0VNqII+8Oe25sbDIXNtiOUM549DfSHgVQi6/UZTdtBTsQiZBSoaqxhtV5juzVY7SQdpoHdfsez5884257x9luP8c7y4vkznn3yCYf9DqjhS5hIaa7M/JwYg3Cvr+7xhDxBcUSzEwZFt50VNk1L5xpWbcvGNzgSjdVQonGeddvROk+pxlIq18Q3bhFtFYTGN/qYIsAaGgC+8SBCa7XJz5QS+ZDwVRAWYwg+Mk6OKSYGN5GngI1CTAVnAkJkyrWqcUkN1sxWOZ2bGdX/LOZt7TU6b57ZMohmO0TQtoBeltBhGqdjTU8ujDER5j6d1cNbirIKtaiLBXjVS63KYlWVuvF+KfW31tK1Hd6paC5V+CYWlTCMMTKGiSFpw2HlziTtPnYKri9NpuYWkVVQp2hmhM8A/b/0BmKuVVjKUksiGcvRKdAuyjEeaxpy1Ma0aQoYjhoBEWEqhZFCTFCyJ0a15CVnUgw4Eayx2gWcgnhL13q8MxhByVTO4J2wbYS+EfIkZL+mWa8pxlNEo71SDCSLdy2mPSMWKCnTD7fsdnvlUoRAjIHb2xv6/sCzZ0+RCorO9O9SClPtF/qFx+wIyZGIZahhm9M0o7WW1js23nPerumcY9V41s5hIqyblq5pcMay7VZ0bUuaIlOY8G2DbTzGGcSA905VuqWeiKJcBylVS8M4Ukp0RRi9xvSbpiPErJssp5oZKXinoUt2FhcKU0jYaLCmZcqZPkamWuKumNRJO4QZ0C41rv+UbaDha1lSuyklJAvdek3XOEpOejjVGp/GeehWlFIYa0dwlYI71g0p+HzkJpgapmpfT02Jr9cr9RBqpbFqgahs3pydmHE3MQbrnQLIFHzX0uYV6xTJuZByItQS/7nlX0xxIdHlWbOkYi73jchXPMQ4KjIfe1HOh+jsMSzf1+BRMpDVUuei+e4io7plqVa11bJvPTnA5ITB4E1TSVEJi2BF29JbA86iHZ6HQL+7U5DJCsY6xUasRXxLGCP7EMm+xbqWmIUpRHb7G0KtEIxBF9QwDDx79oxxHOn7XoVu01FHQAROQ6wvMsr9/y1DEJyAs4au8Wyahm3TsnaezjpWzrFxntVqzXbV0TYqgWdExXvbbkUxovoZ3oK1YKBtVA0r58qRsEIJAzkEGucxRlXFG7G11iSyO/QMU6CJgUShHwYmkwmxQDbEKiXXOIvti/Y1qanvXAqJtCzyUq/t3vVLRf4/bfrk6FlpWK9MzpyVf9PYhvPNGW3jCV2vbQpS4sXtTaVqRwUOrT5ecsLW1o7WOKw1tM6zXq212K9p8N5rdq22S7zbBVVunz2EcVzwAc1gmQU/mPGqhQhVDVDbKE8nJpUcGMextlNIS4FXqojuvL6+0h4EVNQ1qihpLvkzwCi596WgSH1IkZiSTg5S2ZNpiUud8YjRnp1WDI1V99g7S+s952dnbDdrjIHdYU/jtJN0kpFY48iQCqFEclBwTYyvcvmOrvGkEHFOT46YAjfXuyUu7/seEeHy8pJnz56RlzDnZZf5C2Qr6qIvJ1MiUot2RPBi2fqWTduxWa04W63oxNFZx9o3rKxn0zpWG/UgZrGRxntWXYd1nmJNNQ61sVD1yc2sG2oA7ygp0Vi3fCY7L+QQsW1P0/eM40BMCSvCFBxTCJhSN1wx5GLAA2VSMDkX3AxY5qzZ6nnNHJkPnzv1eTpxuWpwkBRX8VY7pl1st4xOGbNt1/Lw0WP6YeR2v2O/P6iXt9gireNou3YBfr21iLWknNntd+zGgX7oCTEQp+OhQJlJePV6RMOp2Uea0+oLbcIYTcMai7G2Nps2dF1XDWkipcw4Tkht+Qgn6c83jK+EgYDjhMNnINXCkjefx6wsLWRSNlAMRVIl8tWMyJJXBpFC23isWLyzrNqOs7MztmcbKLESnrrKg7DkEhmGCWmtdv8SR7fa0K7OmELm4uKBtpinZ73ekHNk6lUByhipBVsDbdtRSmKaBrw/El70Xr5sLD57qJd9X+lbEGwBh8EZw9p5zpuWs27FxWqLS4XWObq2pbOes23LZr1a0rHGWdquw4iSyaxvKNZSjKoxLSSqk5BGSkJyVnVoihoB32ABcZE1Fuc8vm2IIeBqx68pBLx1xFFp6yEkxLcISp8PRYiwkJkQIVdreN8mlJMJ+bRxb9UQY8bU8CUmzX4ZEVJM3A13TKOSwdZdx/ZsS+FYb6Hs02MmJ+VMGEb2+wP7vmc/HLg77ImC4jigjZHzUf16xlZAs51VomhZE3P1aCmFHDJjVq7GaZvG+XtrFe/ouo5UjqH7YmjeMH5sAyEivwD834F30c/+W6WU/4uIPAT+feDbwPeBf72UclVf828Dfx3Fl/6tUsp//Dn/liKwqYI+xpDrxc3FTTMgZGrOXrtrH9OjiaK9KWpmqaDhhK3dsgwsknKt9zTO0DYW54QHlxdcnG1YdS37Q6DrOu29MGpb9xAzQy6IBKRxdJuO88tHpGJJBIqxDGGkiAWEu/0dfT/g3OzGasu+YegXIHImE80x5I9DqS41tFhERbKSq3zTsrKebddwsVpx3rasvWNTOQ2tc6zbFev1hvXa4hqHq0pXGMG3KsdfEFzbUqxqXTin/TtijMSc6ymmIJ4pqmYUcyZNky5uI2AdjRh842nSSk84s8f7iA8BazwDO0oRphKwWVOOOReKhus4hFEMIU2K9Ody0gFN50/evAdes+Bg7gSaknbbyjFxdX2lnd5z0XBwGLjrD2QK6+2Gpu2WDTcXlKmhmxjHibvbW2KIxKLd4ccQKM5AkSWVWiqYKDV8giOT0ogsIOgpMWrGOIxVbOFlmTt9XsEYh/dtzc7IoiglPwsDgaav/w+llL8vImfAb4vIfwL8r4H/rJTyd0TkbwF/C/ibIvIXgL8G/AbwHvCfisivlbnq6lPGwjO/p1x9TKsv2av6gHVz3X1exD6NscRc+yeWvJBsTJWmk6JGwohRz8EajbUbx/lmTeMcKUVyTipDL0JIMMWk4CNCmBLeg3GeKSZSLrSrNTFlutWaIpbn1y+43e2AzLq1VbdSkedDr8y6B5cPa7HXEaEvX8Aw3Ju7eW7mOSssseq26zhrW85XHd1qxWrV0mA569ZsN1t80+A8iBNc47HOknJBnMU0XkM+cWA81nvE6nJqmpbOaThRclkaHM8napimpQK2pAnGAzlMxFRopkmbCdUU4aobkaZldeg5HHr6fsCMI+OkDWJyQVexFCSotxKrUUyfATu8eRwNcSlqJCxwt7ujpMRmtdZDJ0WGaWR32PP89rqGWa7S4GdVqKiCRFmrSEvlM2gzMFMrj9XjzbXGZzYIvh4S2nhJU912rtugYGoaNNeWj5JPKp1rNmY+YDSZVIixVjXPe8X+jIhSpZSPgI/q93ci8ofAN4F/Ffir9Wn/LvBfAH+zPv7vlVJG4Hsi8h3gLwN/73P9wS/gXS+xGTMQc+SqL+F71ljcVYuvna+O6UBbEg6r7usUoBSmcVD5ObQzeCxScQdIWTCNx1hPSAWHsvE0GyEY5+lvbzkMvVptKYikGmKkClQNdF0HkglhPLqYc2nkF13uc3xaX2ZEr7exjtY3tL6lbR1No13JjRG8bWjaDt+oUEnTtDQrh2+8brpYF2rjwRpEWoxdYRuHrVkRYw3mpKlLXo4APX1N7WQuVsjJkSQrWJwSRWBTm9akEAnjiOtWrLqend+x9z320BMKSIykcVSP0BQElaEvIZJS1L7KzCHW55m70xNnIXhDUdbtkAJl6olkVt1KwVhTGOJEqcVqJs/l35V2VXI1ktSuaKcEL60/kRqSWOaqTn2usZUs5fRzuZnmfloCYN1LKX4WFbIZf1OQUlW+RVHX5bnh58GDEJFvA/8M8P8D3qnGg1LKRyLydn3aN4H/+uRl79fHPt84dRk+DaKUufRWvxZ0gt703CUGS3NK1OiSKhkVg6ixnHOEPlblJ83J6ykBKRZCSax8w2pzRrG2algIfT/gfcvd7S1XV1eEYWS7XdF1Dbu7F0tKamZNDsPAixcv7qkTfdExewvzNZ5er7eO1npaY2mstgSwxtB5x7ppWLUdrnWI085frm10oVqnnpOJiG8wvgFnsH6DtSv1KvxRIbmctPuzdfoLGkgXlypbEEryRCOkOGJjVOC3ltGnEBHvcVOksQ0r37LZTDS7HcU67GFPKJGUXEX3A0UUrSfP7XPkxEh82pzJ6Q8UqiRRVt5ARqnNkgtpmBhTplutSAWo4GvBaPUvpWIGx0PJGCG+5AkWwFYDbtH5aLpuabl4T90cNQyzwdGPdkRlF1Kam0vuWSQND32v0v1DIOeISlhUrKZ8upv1ExsIEdkC/2/gf1dKuf0UhP11v3jtRxORvwH8DQDnfCWX3H+LOYQATXlpoVQElDtgTYOzlpxqy7yi7q7GX0b7GlQX0LuOpvXkMdSlkGjaBue1y/eh75fOyCEWQowY02j+2noKCcTSrdacXTwgFQhhFoIx9IeeQ9/jrME7p2GNCNMUuLu70zAjBOaiLZU6syd6gV/cezgu+BN4txQa6+icxyVwMdNkaLOhQdOdTePxjaOpknmmYjlFDIjBNA3FWbK1GOcwTYuxrRqISuoRo8y/2VBpHVfVTCxZm91WhmdxgmmEkpQGb6cAIcI0ISEhvt4X57Cdx4cJ0xoSgVx6hjFVrogWHjnRTmQTEFKBlGsIWGfjhFh3byG+4mbf38wLyakUshSmcWQ3qEdZkGPoKgYxmiVqjKn9WaDMylOY6tFpyX4RsMbSNS3r7ZbN9mxRLZ8mbS4dQmA89IzDcNLQVzREEwgpaQjiDE3b4L1js1pzeX7GdrslpcTN9Q3XVzdc39wxTGG53k8r9Yaf0ECIiEeNw/+zlPL/qQ8/EZFvVO/hG8BcS/o+8AsnL/8W8OHr3reU8lvAbwF0q3XJS3Jnbv4hIHbGd09+Tmq5s4rL+tbS7yvjLwsFMyeUa8erhAFWXtNzIQckRaRkrGtZrRpSztzcXrE+XwMwDJFxDGy3Hc47JQqNExGhW3Ws2o5+iuzGHiMF74XdTqs/zzYrrCRCnLh6sdOO3rMg6QnQmnMihHJM6VYI5gvfn2XW1IUVtN2gE2iArfVsrGdtWzrbYLGaims8rrM432CaRlsFNF7NlKBkEO8xvsXYBuM91jmsdXoMGlnySEJB0lGMTYrRY7N+siJOUXUrZLGIOIQJSp61WEi54KqOZ7HCWjLbsePQO1beUpLFF5ikMAA5GzKFlONL+ayX5uc1tQv1UzH3D1EWJRoKYChYitGDJwZtxlwq2K2FUAoEW2NZt90CeqtmZUBLtfWNG9/gOy0qbJqGbr0mFU2vDsPIYX9gCnp4DP1AjlH7rqCkLuss2SilO6WEjRDigIgw9Hti6BEyDy8v2bz7Do8fPObJk+d8+PFHCrzmrIIzn7K2fpIshgD/V+APSyn/zsmv/kPg3wD+Tv36H5w8/v8SkX8HBSl/FfhvPs/fUikuuffIp3wyoLpc1h2fXhTknPeaMQYpx3oBSsJbhyrtZFJUQLPrOvpp5ObmRsVtaybEeYdHmYNN2xBjBT6rS5li4na/I8bEatWxXq+JMbDb7ZimiZQiIQz3hGKOtSanRLDPM0OfPhuzD2GMpjZNgbZpONtsWHUNm/Was/NzXNvQ1Oa1rm2wzmObBmM8znqlG1MUiLOzB3WSVrNWEXEjemrWE9ec3K7TQrPZcOWckGKxtkARbAOmZIpYimg4kkG7VkcwYcR5x8XFBbkU7nYH9sPAIUQSmlkyWVWgxJS5xOILzNn9cGNJNeajQNBi4gTVFIkJIwVXK02tgbV3dL7h7OysEsvu/x3ntDp2bqiz293R9z37/b7qgsxtDHRNWquENEshLwsErCnMBMF5LYUpMAwju7sDz7cvOL84Z7u55PLBA4rAixcvVIToDeH38hk//7S9Mv4K8L8Cfk9E/kF97P+EGoa/KyJ/Hfgh8K8BlFJ+X0T+LvAHaAbk3/w8GYz74xj3H8erO6jU9FJKarnvaf3V98k548wsEBt1QQGI0rltVYjqupZ1WHG33y+gkLWaRvUocNQ2LWMJxJgYhp5Y5lbskaZp2Gw2TNOkHZpqqJKzqLTcMNwrRntTiPYmpPl1z79HfnnJ+zC1yrVrWlarNW1r8DWFqew+h69YgrEWYxRPqYXC2lvEe6xT9SwNQ7S+xFlLkaoJenJrXs6zL4YQg5AopmaIRMCo8rKUAuLIomLDuYYkiM5513XadFmUf2GdoxwGxqpersYhY3JNtb5m+r4IM3W+N6+L2aUUTCqsWuHBg3M2mw0UGA4H4rCnL4n97g7TtGRjmBXRSlEA/HA4KDU6RO2eVXUkvD2GbdZZbON17sUQqqJ1rhmOGCMhToQwVrp1IqbC7W7Pbnfgxc01Z9tbtttLVl3HgwcPsNZye3f7qc7pT5LF+K94s3PyL73hNX8b+Ntf9G9ZOQG+RFT7AQUhZ0DmdKPklAg1fjsSQXLVn9QuSqYkTAFTTM05Z0gJU/IiQNt4T9epPN3V7Y0CVzHh7PEUsZWDrwi6IcZMP03c3e3wruHRo0eM47CIg8wElRmYnEVrT+YIkcIXWLuvHQtyUXkABu032jivp9pqzaZb0a2tcvytwbUKSspMsjEGKw6xukCzaGpOrMFYQxHtd+mq3oPUDT6X0C/isrN45Mk16m2rYUiaDdpcLwBi7MJzyNnjq7p4LIVV25HDRAiGtFZDrHG9XTZGkKICQKJ0Z6kqTKcFfl94TmduAVGb6Jbq2ZqCE+Hh+SXf/oVf5PLyQQWbn3N1dcXh0HM3DESEhBZ9HssCioYq1ai79ZrNakPTNGy32xM+R1mAYDFK1koxLkYixlgzFip23PcVr8jKmuynQLy5ZuwH1tsN69WKB5fntN7x/icfv/Gav/RMSs3rVmZZFSspYtA9VZfVSRn0zM+fT2Ux2r0qh1gr/WdJNZXQ8FV6K4w9WQpWZLkpSx0Emi46VoipCEysPPrD4cAQoBsnihu42+8Z+p7t4zOGYaDvDwsTMcZYgclRORUzSn3PE/jsBXzqbbzRi6j59Xl72tno+YZ1t6q1EYJxqsHpG6UQ2xPPQKzTUMOrloWe8A5jHGLsYkzufZbTj7OQu04MRK5qzwClZp2MxRqncH5WsA/R/IMx2jKx5Ehxeg3BN5QUab1n1bYqKiwqNBMpxAH6acIVxZ5Svm8YXvbWPo++RjlZVySldxsRsJW+7hummHn24ordfk8Gms2WIEIrKEEsxEV7RETIQYlMq9WK9WrFZr3h/OKczWaLqxT827s79TaniVjyHC3XkJbaXtIpluE7pBis8UrfjpGYImPU4rocA0MYGPoVZ5stZ2dbLVV/w/jSG4iZDTenLgtUMdrTOPH+BpmJIimlWm+vrdLgGGIok2yuuMvkqGlOZ4zm+1OqBTmZ/nBYDI3W7Qv7/R6sZb8/cNgf2MdCLM+Qmz19CEwhVvVpVcJWj2E8gkMLU+4ndBU+Y5yEqkhlimozY8VerGVJjy2xtwBVuQgRitEeEmJmnQLlSBg7y88d5/Szxus26cyIzcYheY6nZ6mUqnVZaz6kqog33lOSpkS9nSiulqRbofGWJhpaVysjc/UmPsf4LO/imAWZjaEGsdZaxhD43g9/pI2OYsA1WuzWrVpW51voR8ww0g+qMeqco9tod/eLiwsuzi8QNDy+ubtd2i3qYZQYw3Rcx6XgrFN8J2cNB60adecc1qlxV6Fag7cGXCbVTmF9LpXh+voDZh5ffgMx/yccmWg17TkTgNSaGubeCzNPKiXd8EbsCRZRy51n6bSiMdxRBlzBLVUwdhwOB+7u7gjTtLjPjfOEMCFJxVdzysQpEdKOqRxIVac9bbRL1m63X/QqU8paTZpmCblXr/inMY4KQ7LMkVZB6uI2M0Og1lCIUVl27Rim4i0mF5ydRXf0TRZ+f01rzjTd2dSZ+Yd711XfoIAsikvqns/CsSVnSm0deFqgXZupIaWKqliLdYXkG22GExKtnygpK3DsPT5ENYDWEgFLrmGPqWHXm6GvN2U2Tq5E19vsTYlqT2Yx3I0Tfd9TSiJKoQyZQ0w044hvtJDKe0eiwSUtiz8727Ldblmv1gxh4lA1R3a7fVVeT0q0MmqoiyjIbEC9mDpRKSft4yJBmcPOUqwadUTAOpyBzmo/2VlQKUwjxzL5V8eX3kBQtLjHm+MmL+VYhVmfpJO4+Af6X84F4wyNbyBGUpi05oKafhM01CgZ3zSUoGQlX/tchBi5292panEqhH4kxMjZZkPjst50p1WOKU5MYWAogFjlEThDIbJatxpqDAfCNC4y7/BqCfdnLdDPnq77cfa8V+eTWqzBOKO5/BwwuaHluBlT0QIlJ1VMRmZhEzmmMI2yJXXxqWtPDc2QOQw8fp7lSmq0YarXMWeMclIuQ04BSVm7QJVEIWMMpBrdWWMp1irxzVklbDUZ36oEvQuOTbdiCgk/BZwYVcjOaTaTfJqn82lz/trXCIvYS6it+goZYyvfA/WG9sNA6RONNSo1L4IRB1j6fmIcb3le7rg93Gk1aN34xjjAUGof0JwzxlmcsxikkrhOe7pmctUPUcNiKPWe5VSFgSr5Sus3dE4/7Uz60huIUgpxnHBilb9A7QNQMYGZMHVsUlKWijjd7IbtqsVZId9GUsx4Z2sqqkp/idSUaCGHiaZpFlbjOCXy3PhFCjEH+mEi2kwTLbH2cgAIMZJFQxSViS/ENDEMPTfXNxz6AzFPaCOVVztwvxwTv6wB8fLvT4t1Tt9nHtrU59hDQRFygzjLSOYQAn7dEQAJEWlqSznjsLatrQRlUbQWa2roIUuJ95xHl7lH6Cx/f3L/TjUHTg1g0cYkGBJFskrfk7VyMkY99XNZ2IPqPUJCdTdwDtN6XPDYXqtUV65hbFr2Q09jBGKo2hRqHD6P3f20bNLpEBFs5YCMYSJLRmX685JeTSloeFxUJi4Bs4jNYYjVg1Tvd64RMiJYCoSAKeCcpetWdK1ntWpwTaPPEXWVZ2xLgDBNyqkpMEyBMUxMMZKiGuoh6OE0E6TK2N+T3n95fOkNBCiolVMmm2oEZhryXLw1h84iNZswl81q6OCsZbtacdjvSdOId11twecXeS/vBAyMYTpyGXImJm0Ak6skuxhLQpAMLi9dN6jBrv5tKSCFceqZdnN6UysYVTru5QrNH2NOXmMU3vR+pgKHqUr/3w0HSs5MaaQ4UTZj24BV4BEsRjzeNWQD4qz+s3b5fgYw57ZXcuJB6IxUbw/IcuLbVc8hV+9BsxZV1WtulWiNVjbmQooqt1dy0sxBiprGpLa9EyVYReuYrCVZR2O0KYyVWmtTCWdzWFp41SP4VI+tumH3uTj6mimoSHGIR4O/vGbO0pz8v4gWr1HnZ67tMcaqOjj6e0vCmMT59ozz83MePHjAN955xHbVIGJV+LYUnFf9hxACBcs0xUWJan/Ysdvv2O/33FzfshsmhpRV1b0oRncII+FTuBBfCQORi6o75XzaFMQsXYrVHTNHwhEsifgjBVaUPVld5JnnnlKVOcNSqqxbzgnrhGmKDGOvRqKoulCpnbbEehAFMVOpW0EKckL+ORwOSzGMtQ7tCq2bYc6QvDzeRAX+PONNi95ULCKVwpgi1/2e290Ob+C6v+Od8BZvi6bY1mdntWxbjWGetdJFlvJ45zzWNojTYO2YiThJ383kIpm7YSkgVmaPtpTFuJjamaKg3bpySce2dNYsgqtmjitzghRxMULURkLROBosSTINFiu2ZlocJkcK8bVz86bxSpbjZMOfGhg1/LourLG67aXiPZQK7trKuJyVr3SOVFTZLtNlbFXQLprav9ye84u/8Au89+432KxXbDpPYyDGQM/AOI2EMeG8045mGJxv8U65LBfnZ6RpZBoG9rs9V/sDT2923O33TEG9i/Ap+AN8VQxEFeQ0Ji8ptdn1nXUT5sdzVuxhJv44pzdg7j8wP69tW2X7hamCNRMlBjrfsOpamsZxGDUVKai8V8wKMFK0KUwyQqpoey4nGEhRJPq0Uk7kyJibP++ngUOftYA/rws8N2BRFzdye3cLRcMwmzPPb655cXvH06sbnjx/wduP3qJrW862W862Z6wfXNAZ6ABvvcrjG4uzGvLNwqdSwTo4PTxrlqL+OIOT1iogWqhofMVtjIOYC6FqOhhUqFicqz1MNA0dQySPqjlaYsJjFAC0lhATTRXY9VUR+uhtffZ8fV7s5xgqzT/Xw6OUOkdUUNWBWEKMtcBPqdZLWnjukFZUoaRQMRwK2a8IeMZsSPuJ589fkMO48GoOhwP7YdSMhUAuBt92CtQ6x3rV0DYOU7QJ0FuPHnNx9oj90HMYBnb9nt3Q8/6Tp2+8zq+EgYgpIUFjuRkfmNOU85gJUTlLLVxRiS8tQda7aI1SglerFdv1hhQnQoyQIylGbCl0q5aua7FWMQQR5bprJ2eDMY6UqhCpLdq7IahYaC4oHbgoT2L+TKW6c/O/I2eA+vXNm/2LpA6PFGZ9c1NTthrva2jWDwMzG9F6T8yF6/2eMUSeX13zj77zXdq2YbvesNlsuHzrEW+9+w7fePwO7zx8m2bVViXmVqsmjdGTU478EeZPIWZpy5dTrgVLNb2s+WpKTBAjpkCcAsOhJ4wTMasLrckCJSZJBazHaSKGibE/KAEqq9s85kQgYxqvmYFxpBkGct/fm+9PswGvMxKvuwdHrdD5NUKKWXU+xbBpOi4fXNKuNtweem7ubul7lfWfKdcqLZdPwjNDrl3Ncyk8v9kxhg958uxavaqpJ8eJaYrL4ZPkBAS26iWXotoPjbNs1yu8Vb3K89UZj84fsWk6zrdnIG9z1+/5h9/57hvn4ythIGCWyIqEIBVV11LWU6LR8Z/Sg63zCLopw8xaFFEXzDltomNMbRFn6KyePAqOZawVnHeVNp2QhZ1iwFiyEUKOTHEgllxbwWkKscy8YH68cOEnH0dlIgBbBJuLbsqcKWFiMhmHZSqBnDJDrSiVHj65utJF+6fCxXbLN996l2+/94u8/e47fPMXf4l3v/UtfNdpOXitYNQmtLNWwWnErou/WPW8iBmTIIVEf9hx++wZ/X7H0+fPFl3HENTAdl2HNZkUJmUaItpKMSbiNFUvA5Xcb8wy7zPxq20b/OgpY8+yoz9r5j4HNjRXVC7YQklQMq1ruFh1vPfO2/ziL3+bwxS4+9PvEnNUIlgpmpWQGdqu3c0LFCZAtKCtQAqRw11m2B2YxgnnKvAdE6H2dMnOLAbCxIzM1Z4FDqVwu+9rOA1rs+NFe03bdWzOtqxWa4yzx1aIrxlfCQNhas3EUQa+5t7LsXhmLhpSqe9AQctfjRTiCc99FishVwJVzhWk1NNOv29ovHbG6tpMCIVxymQyKWu5rxFDDoH90BNq+DK71OUIgvzcx0wmm+NkTb6oUpEVQ9P4upE1oyNozD+ljCHTNL6eYJk8Jo1tx4F0d+Dmo09455136Hd7rBgefeMdjJt1N0zFIl7qDzmn4+qch5QI40C/P/Dkycd88MH7/PC73+H26lqJZZUINWt4iGjPDYDh0EMptI2GhylErIHL8w0PL89ZrVakmqototWgjTE0lTugDv7nm8MvMt/zV+cMFxdbfuGdd/iVX/425w8e8Pt/8ifc3FwxjZN2jq94lTGG7Vb1SVPSVKyRhCEvzYhzUo+kpIgtCZJmkJJEjFXhFxWvqkYqFUqquJOxpFKIoVaPOm0xubu9wu8tzd31QqgKMbzx+r4SBmJesHMc67zDFKOLuFa6LQQeEVLNXpiZml07U9uafpvd2RADgorGzkK1bdfSrTrazjGVQtMkmkY57iFnYk4a8mSt3RjGoaY1Tf271Z0nQ7HH9OtLKcuffE7uU61fcYtn7AGwivnTuYbH55dcnJ/RNApspZIZpol+1H6RMUSy0cwDIngxeNES8elw4OkHH7JdrXnr0SM252vaVaMEipr2NFU09bRAbm6Vl2MkjD1311d8+P4H/OD73+PJhx9y/fwZQ39gSlpHEFNe7lnORUvLRbUVMgUOAyUlpnEixYnGZR4/Oue9d99ju9pqdaUoqOkoeNEUaHrtvl8SsD/efdCbgbOWVeN5/NYjfvGXfpG33n7MJ8+f8+GHH9D3B8S4SggrYFRJzPsG365Zt46VN6xsqqSu2rkMgylCnBLjMHCYAkPSTNSUtCfIUMpRp1KOKfeSdb5SNRhOHFmEMY8EKYwhI7Ud+aexTL8SBkJkBncyuVDpyxHEU0o9vQWMLTSNJU6ClBFnMzlopdsM1BkplBC06jAEpjQRcsCWxLrVjuAr35FDxiSVYbPO0K5arcefMmEMSt8NEyFnxHgkB8QI3nlCiJoarQxNVaE6Pbt+eobiOEcvLfBSsPOJXtQorhrH4/NzvvXuOzw6P2e1bhFvGWJgGAaGQ8/1iyuGYeAw9IRhVCpvyZgMvgiNF0wciIcb4u6avGmQVUbQsvCCUMRByWS0C1o0Klob08Sw39HfXHPzwfuEZ09ZhRHTOoJbcbPfQRCiCDhHRhhD5DBNDNOo8vNNQyqZEOqpZw0hDAwff8zu0POtd9/j3cdv4cXgTMG7zLprcL0Qaq8U7hWXzrXg2nf0Cxd0yVwh63h4fsn59oJiLB8+u+L99z/i+vpO8RPREDTVtUwO7Pd3nJs16/Nz3nnrAe89WrPyls63dM6r+rjRlgF9f+BuGLk99Fzf3nB1t+NuP3A7TPRTpB9GhhSVGFVzKJiCmKQxmNXrdCJYWDrel5lT9IbxlTAQcAowVSHQmo703tO27QIUiXJ5FvAnJeWwm1jTSjV7IGiTkxwywzTSOam5efVFUy6kqBbfGKOK1yKUaaxCG4YQtUOzoEBk4xswlmmqJd3lfhik7vdPe17e/IYzDFIqg9BZx2azZtU0SMqYpMbLCVx0Kx6tt3zzwSPGceTQ94z9wH6/Y5pGCElf4xyr1iElEvs9sT8gtuI2Ronsp9Waal+SitBOI9M4ctjvySHQGkOzXuPPtsQUuNxslUrsPKPSjTCuoQdudzt2ux27fs/h0JNjINfNJkYosXBzfY1JmUYMl5eXrJqGsW1ZxUzjLNOUjoKxJ+nY1wnSfS5wmKPf0TQNj995h3a94WbfM4wDz29vKcbgmpYiouB25YJIhnGcuC0Jg1VxIRN4dH5Gtz5jfXah3pvTeopNijwywhgCt/sdd4eBq9s9z252vLi64fnVNS9u7rQfSzUQzhpWVWvUmoyVAqVDauo/xlQJVm++1q+MgTgdR+WlfE/ff6ngrCeAkkmoDV/iwuqbprCw0GJtV+bF1TqJVAlWpXolGrpM09zvgFrqbBa8IeeMc0qvHqu6j6b43wz+/DTH68KLUkOrIqqjqDyITBgmhkOP2ECOEyXrBrPWkp2n9Z618Ww2nrw9Jzx4QD8eGA8H0jAh3rDdtDgSceiZ+gPWa+dusV5Tt3YWbDVAUom5mCvwqwS1zXaLrz0/LYUcQ21OK/imw3WebrWmXa0p6zUJlZq/2+958skTXrx4zvXNNdc3Lzjc3HC4u1Ns43Dg+sWVqjlZS+sbnB1pnMUF1ZxIM0ltnr+fcP7nMv6QCm9/81vknLi6uuIiFWzb1Wa/o7ZJmAIhDEvF6hgCL65vCXHk5uYTzrcbHj94wIPzSx5cXGo7PhGsFZpW5zfkQrFOqebOLeQ15x2qseEQVC5w1XU4p+0bGqfixM6qZ9n3Pbvdjg+ePnvjtX3pDcR9lSW9pbNBWOrzK0g4fz/3LgDFBlQxSkEtNRyBSX+5GJuU0wmmAV3XEaVwmEZiGgkhMkwqxBFCIJX5c9SSaqO9Ooj55PQ5Lr2fVSbjza5wRQEEUs0eJAr7sedmZwnGsrtNhHGqMnMNjfds1xu26zXbszO67UpDkOHA/u6OoT8QY6RpHFISod8Th5602oJvMClRzDL1ekDngsRCCnEpcW/algcPH5CmNdZAnCbGYSSMg2JGtcmLs0aLoaxSmi+2a9596xG/+N67hDCxPxz4+OMP+eBHP+STjz7ixbOnjIeenBKH/qD1NUXBaDVEk4rD5lTVqgBRPZDPk914eaiDpp5rt9pimoaruzu+8Y1vsL18wNvjyPXNNS+ePef29o5+GNnt9hz2t+QQVbI+B6AwhsQ49lzf7vn46TWN95xvNrRtS+M1G+OsoeTIlDIxQz9FDoeBfhzpxwCi7QyUCatVt23b6PyJih2tuzVdp2SqfXPAmqZWKL9+fOkNxOs2lq/y38A9LUeohgRt1WesWb5KrqWvIriiPPap6kh458klnAiCajYjpcR+v+f25pZdHwgIqfIKZvkxI6a61vpZUkzKpDwJLU4Byp+WoXidfsHy2Ek4NnP8ixhSKdwNPYbMXcyUqCSx1jecn51pOswautWKs/Mztpdn+K6hiJ7eN9c3XF9fUVImRc0OTUNPEyYc68p2NOrizvJ0WCjqpZU0i5wkmnWHWbeklOhzIjUO16hQSte2tcDMkMjYHJFYsEZbIxpn6GxLawTz+C18Tpz5hkdn51w/fwG5YMVUEZWiql5dx10/Kd8li/ZRFdQT1Ml64/15nRFeAOL683qz4eHDx+x2B66ubviVX/0VnHM8fPiYzfqMu92O3a7n6dOnrFadVqDVtTp3UxuGPfu+5/ZwwFnL1W1/L31vpVa6GgOivVlUJNhiXEfnTGW7eow4RCzeq65GKRqyhVzIw0QpU+0Q/k9Ab86Xb9qbxD3mx3PWGFgq8j3fiPk5R4RfvY5MVuGR9YrVWpui3N7ecnN7w93dHfv9gSLqshmnNGuJEXIiW0OKmZi1KbB2/5ozGfeNwz+OoYufpVKzH3qV+M8JKZnWO2KGMvWkwZCc0JxtaHOEsWflYLXZ8vajxzx4620+/OBDdtc32FQwOEhQUoAU1VsoM+JhoVRhmJri1NSyoW1baBxWNMPxaLOufTcyaQwKjnLsDlXGHpwlpwnxru5lZYKunXC5WpM3Z+RhIrQHNRDOE7KyKgOFrvF03jIlbXyUaipail2wiC8SbJziSpAZ+gErjj/zS9/i448/5tnHz/izf/ZXaEyDdw3Xt7c8f3ZFTjCebfFGu3m33hNT4Pr6BZ88fUoxN0yT1gPNNagzpuPdXA2q9G1jPb71eN8g1uGMVS6mOJxtcG72ICAX5QAJVXEtBPWareFo5l4dXwkD8brxOqR5ph/kUhCr9OhJhsptUgFTI8r8c8aAMbjSUIrgrNTTtjBNFaSbgrLSrNVMhTHavbsuJCOmqjkDE4Si2IOqOCsQVH4OYcbL87J8DxpuVX5XFghZacpSMt7W095A8obUWkJjSSvP1BoKiTD0SNuyMsLm4oK3S8H7hvF2r9dZRPtXljI7DdUgVQe81JoZ1Hsz3rFaNaocXj+nc45cCofDjhB6DtfX6t3U1DQGXO1VOldQ+tqYR1Ji3XRw/gBSZjoMTMNYO1YVvPO0Al3T0HgHh0H1QQr3Cspm4/Dj3SFht9vx4tkL/sy3/yzf/MY3+eTJJ1yePeDBwwes/IrQJcJZrkV/ia5tWbUdXeuBzO3NI7rVGc+ev2C32xGCKp2frnDvTE3Vq5KX8001ABaMpfWWxhu8a+m6FU3TKdHMGZCElWNYDjCOA8+fv+D3/vC/f+OVfWUNxOvGTBEqFIyz+MYrHbuU2pikqDwXgnPaYbnB0xiPK5lpnLi9vcNbg/Ee59T6WxMYQiTP9OsiSCnYkrFOcE2LM9qrU1UHlUKbakclvkja7DXjxwpPqlHQDLAaiQxMJErUs8lJYb+7wxnL3TSxGnrOx4HV5QWbRw9ZbbaMu56PP3lGwPAL39iwvXig6cIijDd3WuJeS7IpGbP84VJTy5lctPdoKQWs0K46TBGVlygFEwvPnjzhyZOPONzckKYJW0HlUjJDDhTQRj7Gst2sWa83ii1VirwzQmMdZ5sNo3cMMZCnkZIKrXd0ydN5jxVZqkulSG1uU9fMF7wvR6k9rbO5vb7msLvj7bffJowDz599wsX5GeuurYeXhgXkTNu2rNdrrCSsEe2Dur3k4uIpz1+80EK/FE/WjTYQVm/H1lIDFcWZ0+vb8zXnZ2varqNtO7xvadsW710tGxAVTRbBNw1hClw+eov2v/xP3niN/0QYCKWqJkzREl/xllXX0HlPbDyHcYQiNNbSOIuRBGWEEtg0DavVhhBHchiJZLquo21X3B1eMIWAUFi3Dtd6xDliLoxGiDFhnaZZD1l1K4yzhDhb6X88bMplXkqlfAtEsjZYEUMIGXImj0XR9AKyG/DuDmOe8f4nV/zC+x/x63/+z/Nnv/Etwu6Oq2c3PHrwLtsH56wuC/1+T7i5IY8Dbgz4LLRubj1vkWokYxiJ08QwDmQpWiTnGxUqoRAOA7cffsz73/kOwzDQdQ1t29FtWu06Nk6EacfN1RX97oA3hgcXl5gHD9msN6qFkAKSJ7wtbDeN3l+iukyVh9KIaFrVCiGpYHHGKilJZp/w8/As74e1Za4XMYbDeOD67pqHbz3k7W+8zd3tLXeHW4rZst6sKc4ylkSags6RNYjxKk3nPKvtJavNlrOLC27ubtkPyhyVuRAxaZXr0A/s93sthHMG7xvatmVzdsbFwwdsNhXLaVrVGK1VzHNLPjGKXeScNc3cNG+81q+MgXjl/K1pyPm3pbbNU0/BqCgMCe8szgplShhr8CqCpGi8hc1mxXaz5eZW+RLOO3zXEnOm7w/EFPHOc35+RrdWhespJnY5MybYrFb4boUAu/2eFFI9ueVeWPvTBie/iEdSRPuFTTGSo/ZjtAheLBfnl4y9VgeWUnDOMIbM7/zD3+d7P/wR/8Jf/Kf5p//cr5PGxNWLG9YPHuDaDpyqTieBmKuICxy9h6wl+ilMxBBUys4pxyHXUv00Jp49ecZH3/8+t1cvKCLEEnHecTXuGGs7wlAm9tNIIXPY9+z3B0I/8c133tWqUq9NgKVELBkrGUvGScGLKmF7a5Qp6x1D0g5qUkv3KVLDDeE1K+3N87rgELXZUZy4ub3m2fNnbLcbpjjRDwftjt42iAVjhbFkxjFQjKFtjFad+oZu7bBemxStz7f00wgcPZX53h8Oh0WvchxHNXCmqCizMdjG03Ydq04rO42xmhmKEVuLBU01irZRwZs3ja+MgViGLOUx+n+pCb1y3I/eOYzx9OOoDWudJ4ZMIitwRW2UakQ7KOeEqYQhKarLGGNmPwzkUjg7W9N0nVY/AszcCa9NZrr1hmaz5np34DDefWoz1H8cY9mzORMqmOi8Vmz+8i9/m2fPnvH973+fVBKdb1lt1lzd3XBzc8Pu7pZPPvmYty8fk6aR8bDHtUKMgRQTVqzyRlLWfzFRbAXAolKh8xiQmLTLlDha48kpcXd7y8cffcDHTz8ih4nziwuGXLAIu2Hk6fPnWnBmMsNhz7pptRR8iqRnz3Ftx8XFGa0YmCISM7YyPie0l6bHEMg4UenBxnpcmSpOMq+Y2hbhNYSpzx4ZUCr+4XDgyZMnGGO4uLhgmlSd7Pr6mu3FGd1qQwqRGEb6ftIK13WmdU7p5Kgy9dn2TPt+zvUoVQXMVCMx9D13d0ocu765ZuiHqneqLfisKP7WrlbVQKiBSW5mFctS5Hha8Pi68dUxEKXUXgvzkJMbOt9WJQQp69HWVmW6CGbZNGs1v25kbpseubm5JowDvmnICWJIpAVNEELMKt9V3bO5Z6b1TpvOeEdjVCg01oYmqRQ9neqh9JOoR92fhs+3hE9B3EqoVMFdo5qG2/WGi/Nz3n78FuTCD7/3fciFu9tb+kNPkcK3fuFb/NV/8V+EceDJ9z/k4uKBFm9FSFXvQqyrhLW0GIhsIiVPaiCmkRwmJGWciOoyAv0wcvviBXe3t7SbNZvNIw7jxJ/8oz/l9m7HmCO7/kCIAesVBH108YA8BYh6fz64uqK0LQ/E1ZaJCStF04ECTiCL0os9hpVvqtS/QU5k1mZn795S+gJDb6swjgPPnj0jpVQbMGecc7Rdy9n5ORcPHtB0a00Fl8R4OGCMYbNaMetIWhFWbUtHu4QWxhqwtb+LCFO3omtatqs1rfNcXV+x3+0hc1yblZDWtq2GM6Lrdq5XMrXXzKn8wOvGV8ZAzPUWc9qOmkKkshlN3QbWaIenxjm8daSsINpczDW3VSfXeEws6pxVZesM0xRJokrFIUfuDj2poGQVb5dJdY0nJG1Bfzdq34JU8tKXI1c37idNc35aWPFy9eTLRVxGFFCVrEIt1uhusFl1C7xz/Jlvf5vrqyt+8IMfaO7cWFbbNX/5L/1lvvVLv8h4c8PTH37MR+9/wLd/9c8wDAfCOC0iPBpOSY0sIhIMJU+E4UAaB0iRkiI5WBwQhp6p31NCxBnDbRh5/uSap1e3fOeHP+DjTz4hiqFdrzj0PZmRxho+ub7B5MxZt6YYS+l7uv2BzqxoUtC/kao4sRScE1ISBaURGuM426y5HUf2eSDHuU8Kura+oP2ejXBKubYftIzjyLNnz5Z+myLaQ/Pm7pa7/YGz8wvWXUfO0PcjYww1w2OgnvhzDxUxszCw+ilzk2rXdNgieGNVnDYmrTnKcfEW5q9zz4tSir5306i3nbWH6JRPurC9ZnwlDMQMIp1eSDlxB+fmtgZZmJNN09BYxxi0yWqOUTtSC4h4nPGIJFKOKvoTC1OcKKOFNDEJTDFUN09JQU3TQHHkJBjjKFmFbZkmXtzcMI6D6mfqh64Vdj+/eVoMiFTjgKm0a5Uyc1bVkEuIxMPATXzOH/7u7/Htb3+bX3jrXVws3NzccH5xzm/+5m/yi2+/yz/4B/8AxlH5IGnHcLdnyiNpCpAKOWTSGJWdGiZsyVB0o+ahJ04jUlWrUzSkNAGFECf2hx3f/8EP2Q0HHr79mH/+X/gNroeR73z4PueXD3nw9mNefPe7ZDIhZ0LsscA0ZVIxDKGwWm95uG5p0T6e2uSnwaUIGKzJGJuQoKGLlvNbnNgl9TsfPp/XjL964uqBYCQvqmH3eq6Cps3HiZurF7RtR+NbUiqs+gOEgMNgHgBdoXEq5mO9r53bOCmsOpaLe+dZr9aErXpz/Tjc03aYDZjun4JB2zlQTgT4PsMqfiUMxBuHoCivqEqRFXWr5vRmqdTrECbiFEhVV4JSyM7iGq+g0bBnqhWXMUyUJAw5MwW9wdpmXdHi1Wq1qAaHFCkpEVKhHwcNL2bTtZAahU810V/kcr/AETeHXoqHZKxxtN4rmcY12u6uwOHmlu/+8Z/Q+IaVb7j4xnu89943+c0/9+ucXWwJkjhrGtLj9xjvDsRxYpj2xFEBxBgj4zgyjhO2GTFTILqJnCPjYU8cRxxGu0lZs2AgKSfe//ADutWKX/61X+Pbv/IrbC7O+P0/+RPEWK77O24/HEhOkOK4OD/nW29/gx9893schpEcb8kBLs7Oud10tN6SJWONpWlXlBQoU8DEhE3gTMHWNLeezlUiT6oR/bHviX7NORNyxJi8sHBndx+BED1TBV2ttZqGdC2bzZoUA0YMjdPuZW3b0pnVSXbl5L5WAef5feberzlnZG+Xe/IyDjY3nsoLka/cC0HfNL4SBmKe5DeNOc3UOG3q2jYNJSs9eBonYtTNn41VBd9pJNNgfENB6y1CnACHLZEYihqIpLTpxjWsNme07WqJ50IIZOfIRVu8FVCl4HLUf3gJNPn06+OzDcAX5UOUWmPgROh8w4OLSx5eXNI5T+saWusWj6v1DZTC5eUlj996i0fnl6zOVrizFZerDnOIhP3EMPYMh544BT2JYmSaJsI4ERpVgUpRKCUShoEcJrBeNTSsamZsNlu+9/3vM0wjDx8/5snTFzx4e8/l48f8s//sX+L/+/f+Hh/fPNWSe6M6jb/253+dv/jrv0GYAu9//4eMU+B2t+PZ8xe8tWo5vzzHtxbbNFrOHB1ZDDZnfK7xt2hV7pJhAl5xTX+MUcqc0TjqjwJHOXmpiuLTpPJ7OXE47HGuYRjXhBgwxrJerfHes16vqybnUaZ/3vjzv1OtU+fc0oV+DFNt3nssTjTVCylUohZl+WwKUP4TgEG8zsQrU1CgFBrjWTUrGt+qJsM4EacqcFrTWRnDOCVCCpQMbeMoIUHISFYR1lgSU8lMOSsbsOjNLcYSCsRh4nbXczsEpAXEMOXCECb6aSDkQGEWc/35TtE8Si5aheqsotpG2HSe9x6e80vf+hbvvvMu2/VWsRtjcdbTrVbA3AjG0q06/GZNs1oTyRQmsk8MhwPjcEByIYyhuuoZStLGN8WSszCNPWPfY3LGee1UbY02N55C5p33foliVlxdvWA3Tnzwo+9zGG45Oz/jf/CX/hJ/77/5e1zf3ND6lgdvvc0/9xf+KaZ+oPMdl+eX9Ls9KSZeXN9yfbYhPDhn1a1oViu99hBB9pUdC8M4YW3BmIKzaEVjrKfs5zTkrx8z3gPUvhhKGjv5dYF5QYhQDYnUnhkJ7wx3dy0vXqxZrRouL88oORCnqHwREXLU8uwpBIZpJIaAsSoeLNYqu9R7hmnUg3GaNNOUHKVop3YRQ6wtFzJzcx4+9dq/OgbidIgwN4yuhF495bsVXdPgnSVMe0LKVQ6cWndRSCUhQQtl1l1TC2AM3lqKqD7lXCEvtkDMhBDZHw6amUiZYYr0MZNR1zCmyKEfCSFRStWVqEHtjwN8vW68Qit/TVZkBgxLUYUj5xwJ1ci82G653G5YNY5HF+dcXj5m7u9lrVcp+4rdeK+y6eIbUtFeISVFYhgYpqGGYerqdm3HgweXbM+2NN0KiqgQcK1vUFA503jlTsSciblw/vAx7eaCh3fXvPetb/D82Sf048DHH97x6OKCf+rXfp0nTz4hl8zFw8f8o9/9hzx78gk3NzcwBloMzWrFw4szzs7OWG3P6DYbfNtp96kUFrBO9RurnLyhzo1FxiP7dsaxvnio8bLGx0vNjk7rKWDxCDTUzYRpZL+/wxrLqluz2aw5O9uqrokxS+eunCHEzBi0CW8IU+2tuqqak8duXnMBY0qRGFXs2eYC7pi1mD2QeFKg+Lrx1TQQLw3nHKtVh/fHtmI5qUtHKdiismu5uoBzutNaqz0Z5Ng/Y3bBarXVMnkhRKyLlFSYYmaqwqE556q0HLVhuMx5zZ/+dX6W0lEpZen1oai6tl4zKfHw8gHvvv229qusHbe6zUbV4oxXlev6GussIkVFXlJSoDZnbS9Xm8mOlWOyWq1oG6XzWquko0JR42SrsExOyjXBqZydFSIZ4x2b7RaIrIcNFw8uyTkz9D3f/qVvs11v+OSTTxiurtlNEyvnsd2KZD3eOi4vLri8uODthxesN+e4rsF4r2zORmXvQoyLurikk9Mejqf7z2B8XuHbGCPDMGBkz/X1Dc5ppfL19fViHLxXTC3lWo1Zaeur1YquaypbssFYwxQD19fX3N7eIFKUzm0tzjV0Xbd8pjkEmaZR1cnfML4SBuJ1MuSnsIT3nlW3wtWc/DRGLQaqxqLkvGhaSmW+tXMaqiSCtaQYjynJokVG2gRFz5iQIjZlwhTpQyCmrG3ma0w4d93iUzbw573Wz4tFzHNx+vipaE5KqYq0GLq25e233qnCtR3GerpuBVZbxzvfLKI7ygzM5JJqxybV4IxjYOwPqt8wDVjj8K2KliBWi+SK0reX2gtmD7ui6cYwxkh2WVsTuBXO62eYhXr+3F/4TVJKDH3P1fPnjDfXeITx0PPxRx+DKKX+bLOl7VpW647u/Iym9arS7IQUJnLfU0ypfUTnvq4zMCfL6f555von5bF8mmcYY2Ace54+fcI4DvT9gbZt7wGJxkptslzp7M7x+PFjnP0mbeOxrkFQ3c/9bsfVi5Gb6xes15tFiHm1XrNerdFu80EbRFn7s5WcExEL/HfAB6WUf0VEHgL/PvBt4PvAv15KuarP/beBvw4k4N8qpfzHn/Nv3Pt5preCKl43tYXe/KycIpDxtUowTlrso66V6hFIjVGstTjvofLiNS2aMIlK587EnJnGwBjvCDERonoNISdSqrwH5VfXybZfOLT4vONeteYbQgxXqbMpJQyw2awBWK1WXJ4/ICVwXlF0bamnMaqR2pMh51rDUVO1ORGHQVWlxolpHJjCxHrbYpsG41SLMqWkFOrKRRAyxkIxFtd6bNuACFJTccXUPmS25fzBY2IKlFJoN2eMIXCeEpcP3yLuriEGxmHg4q2HurFzpvFNZQx2rNerRSFapGialVK9xaNXuaiGfUZHqXlzzizDn1XJ/mzUx6lnquDiMByw1lGKft6cVdtTRLUerNe2DeNhx6ZrWLWeFDyHw8DdzTXXz59xfXOtkox1DxjjWK/XPHr0SMHlpCUBZ2dnxJ+xqvX/FvhD4Lz+/LeA/6yU8ndE5G/Vn/+miPwF4K8BvwG8B/ynIvJr5dN6sdfxJv2HAouKsuadVdZMckRItczVqOpTXRhSwFqHAH0/0DijuozO0XYrGCbGNJJLqJtDiCkzpahYlrUUI6oqnCJUPqcSNvNP5LZ+3lPqdQbz5d9P0wSgLdguLjg/P8dax9nZOeOU8K7B+w7jrXpOcjwpT1NkzlnCODIOB8aD9jYdhgMhTxiv+himqlinpJTmFCNxHBBUiq9U4zO3O5T5MxfU63JmSdsBmKbDJm2W03SB0DlynGhCYPXoob6uHMO4WdY+hbESlwIp1KrVahgUbK5kuMIracDXDfWmfjaW/nSej3yFQowT+0M92dWlrUhR1gIBM1F6lNGbAmIgxMBmsyZMkZur59xcveD6xQvGSTknMWgPjbZp+Oj9Hyn/I2eMNZyfXzAOwxs/509kIETkW8D/FPjbwP++PvyvAn+1fv/vAv8F8Dfr4/9eKWUEvici3wH+MvD3fpLPAHMKacQg5JgwlSg9GwPEYJ2h1I7RM7sspcSUI2Ea8YCtnIkQVbW61BZwKgSjXZudMcSiVOo8cxzKjJjWnhgnRuLzLK9TpuSbUpmftlBf+3r9hRpRa+jajpwKznhM2yE47V1pbeWSzOFAmqFFCpUrUDI5RuI0ctjdsru7wTiHa5x2/K4d0kW0z0iqylHkiDctxkml91aK7z0QoHaDkhkwRHEjYxCPeiitaBPfEGmKSgYS8zK5ZsowBsBoaFikUssF6n0s1K7wlYV7f5R7X1428qfz+9MuupuNwxxqKAXQ1UzH6ectQKJELfvWcDDyg++rVN2jR48Qsbx4ccXt7RXDcFhEaTWNKoTg2B/uAFUZs9YyDH01JK8fP6kH8X8G/o/A2clj75RSPgIopXwkIm/Xx78J/Ncnz3u/PvbKEJG/AfwNYDlV3uTizR2Sx2mgtV7dWxIOXbdUSy2VRZgqIarkgm1VMHXMenNMCIQwMYWelJ3KyuWiDUhylS2PkSkkYj7C3scteYy5Eebm4585fhz39T5SfvJ1pj6fZDQOh56CcHZ2iRFLjIW2cwiGWGolbNZqxiJuztlRTISYSSEo/rDv2e92DP3Aw0dv8fDhQyUlaQ0tAqSkefgwTUgaSc5hvLbNM1k37NwYRl7al7NhUuJb/d4URNqqLB5VALcISG3IjFDiqJoTM7JQ9G/YAjYVpIYUsYYYuRxnrswy9MixQW9RVbI5C3KK6/wk92m+tpfHfJ9yTig9YcmL3veYT4y/ESGnUI3BnmdPn9CtNhwOAzc3t8SoYkeayYjLdcz9U3MVzY3Vs37T+LENhIj8K8AnpZTfFpG/+nle8prHXrszSim/BfwWQNM05VM3UHUdYyw4TA0jUi3QMziTSSWpxHspOGvwTotlyEa7L1tXmWaGpmnpKs6QgalkQslMRROGedLfqVv+ukt8deN+3vHjnk4KvlYMYk7cVefGiWW/O3AYJtrtlkkK++lAs1mBZLRL2ZGxZ4yp2IuQs5BiJFftyd3+jkM/4do1F2+9w+Vb7+LbLYhiLiUnShjIw44w7CH0NM5inSHHZgGOTZ3ruTpR3eg3zUOVUDOGbCyTTPjG1nqCguRMlkAW1Z/IJZJzVK+htugjFoiFFHLFjyCmWn9TSXZ5TodXw2UrGNi2bW3EPP5Y9+Zz3b+XjMAp2Hw6ZtLTaZqylELf9wzDgPd3xJQZhpG6Kpj7sswkLmNmJXbtk5pz+pmBlH8F+J+JyP8Ebf58LiL/D+CJiHyjeg/fAD6pz38f+IWT138L+PCL/tH7k6Ypnxhira2oxSlRWWikjPWC96aWOucq2aVdl7QZbESMKvNYq6XIU4iLyExMWiaONaRUyFGVogpag3+PDlu4R62WVz7vZ48f10jI/K9op6WctQ1bKjBkbW6brWGioK2nE4jqM5LropszD7P7W0MMLzAcdtze3XLoB979hV/k8bvv4ddbrF1hxEIOpBwhjpTQk6eBNB6ITYv1HvFeZe6c0yKkmmbGKj341NjeP7GFJLUJsXXai1MKJSqFvGSNx8VCnhIxBsI0QgjacnEKSoZLkFIhhEKIhSklva8iiK1tEmKugvFzjxWDNZ4kcZmXxSv8jNv0Otzss+7t6eZ/3WtOAdPZiJz+fhynE+/o+DHnJtclR9I80fV6wjTyaV3mv6jK1jJKKf92KeVbpZRvo+Djf15K+V8C/yHwb9Sn/RvAf1C//w+BvyYirYj8MvCrwH/z4/ztWZpeVc7UnZ7LVwGMcTRtg28czmlvgFXT0DUe33RVtESj7dkVz7VuoczvTbofmtaboskNXZxvGnMnq5/pkCqYNGdPOEaq8zBOU1jDOHJzd8fN7o7b/R3dqqtpxeN7nS5Iqe9rBdbeI1K4vrnh0A+sNhsePX7M2dkFjVfegXiHc8pNMGIpSVSvMhVIZfmaau2KzErgszdhlPg2/xOOKAh6e9XdF2oXbH2iaoHkaiC0JLqIno7jNBDCSJpGQozELCw9XUUQb5RslxOkiMRIS8HlAjHTidCUorUcmEV35Gd6S19jQGZDcGo0X5faXkruS5oVD48p91KW9ZurZsS81vU1b/5MPwsexN8B/q6I/HXgh8C/Vi/k90Xk7wJ/AETg3/w8GYzXjWWC6ldbWYPWWVU0NgbrBCPajNZXRmHKKGBpBFdb1qekselMxzbGKvdBFNm/v/HktTfxZ5TR/LHGK/Bb0UV0dXXF9fU17zx6rHUXKTMMg/ZRmAHVBb/QdzKic3TYHRgOPdYYLi4vePDoEavtBtc2FKe9OW0WSmqINd2sYrairv0UkCaSqhCrtXWDG+2BIbVUfE6r6oc5XoCI1JBRMYEyn7AzGUZYQFArClJqazmlFCcUQ0pFPU6hYEXwRvtFNEYoWTTzkbWPZWOgSOZ83ZEFhhe9MmlRQ/F5OBRf+N69Aef4NM/jFYyD2ZDNTaRfxTC+iFf7UzEQpZT/As1WUEp5DvxLb3je30YzHj/RmMEjii6MeRMYY3FO1YFSiQqdOWUHYi3WKBjlrcUbFYRJKRFDpIhFsgp26GlRvYp6ms7/gJPil+W6ftJL+pkMTfnphnz+/Dnf+973+MVvfBNvHeu2o69FbIoHGF348yYToGSmceTq2TNKTKwaVdA6u7ygW68xTUNxXl+fDDaFqvLtcMYRiyXHQhgnaIIaiLnGxRoVATZqXAxUJez7G0Lq/SDnI3gq8+On2Rtly7qaRqXS7HPRArqYU81kKAGMFHElYxtLKwaD4/LhIx49eMz1ixv2hz3GOd556xF3/cCTZ09JL222n0ZG49Mo8296/svPfeV9yuJ/vXZtvi7r9abxlWBSng55eRFVkEuH5tJTzOpGmUoWcl6zGBK1stMZklUeeqZQrJCNkEQxiSkGcpHaRl4XbiqJmGun6i+Ty/Apo0BV4oZ+GPiDP/xD/tnf+Iu88+CREo2cZxhGjPNVmqzm443grCFNE7fXN1zf3HB+fk4OE9vNFu8002GdQ1qPtQ6bPFPJyGZL3PSEQ08YA1NlYg4i5LaBzZbu4gGmbnapRl5vpyw40uniLpUFixVKVvXLJHkJhYrReCiXrB5kUlc61XL/KUViUW8iF+27OoNznW9pMqxXnv/R//Cv8Bf+3F/gv/tvf5t/8Lv/gClnLrYd/bQn5QnE1mrdoyDLT3O86f0+a0O//NjsQcD955++/ykH49PGV85AwKsnzKwSpW3QCsM4Qp7wq2ZhTJ5EbQtqvjSXra7naTeshdNwmrpkNg4ziPflHCfnHNY5UpzACs+fPeP3fu/3+Mbjt+guHyhSvzSoSRjjFq0E5z3TbsfTp0+5vLhk5R372xtWXUfXtjRNpf06g3MeZwrFT5SmwbYNbr2hzRkvQiiBXQik3Q6z3/OwaBWuqe0DMMf5fDmbsbjzYhApKs66IJrVo6As945ybN6Tc14kADOFUOtKStFiPWdUgu9b7zzmW4/f4tHlGYbEdtWgNtDyjW+8xYv9tQKwn5Fy/0nHZ4UXn0WQW553kpX6SY3YV9JAnI4FdS/qPsYY6ceBkkZaP8tv2UpyPKYXFnBnRu5zWTpyqValNvFV8AcWcsPPGKj6aY5SVJhFUBB3mib+8I/+kF//lV9ju1rjV4rbzI2IRWpbw9pK8Obmhv1uzy9/812mQ8+w32ntSUrKD4kRJkPJ2sEsxKCCOd7h1mvO25Z12xDSgD307GJkGLWRkSm1I/3p+v3M6a1m/aXn5DzfyyO6n6ZIiklVyHNkjEGLxgQNJcl03tM5wz//T/1F/vI//c9w2N3y9JOPOOxuoETOzs9575vv8Cc/+O5P98Z8hcZXzkAsxSv/f/b+NNaSLbvvxH5r7x1xzrlz5punqldFFoeqEoeiSKlFoZsiLdlSN5o2YDVkwLBsCyBgCK2GP0nyF30ioA9GA/3FBgTDbhlwt1puNJoyW1LTVJuUKI4iWcOr4VW9ech8Ody805kiYu+9/GHtiHPuzXszbw7v1UuxN9599+Y5cSLi7IhYe63/+q//EoCMk1KRSbY282LKPl0bmbQdilCFQEwdvrBnvEDuTL6+jYmcldo7XFaSFAORMylDzFrSmn3GgNMJjMfsRlyGWGMbFnR/7TwKzje85gRUE945UurQEFgu5rz37js8f/Uqe886shvZih9cUVhKOHHkrEyPjxhV1jzo1slNvvvmO+xd2eGpF55ntLkB2pFiR+cagirdckk3X5DFsbG9TRCxpjHakaYnNEfHp8qLVUoWafhO51iIkq3qkfkBw1TjQEgube29gnbErrVeGp01Wl5dP1MeVU3E1OJQKoGRE/a2ttjd3mZzPGZ2PCO2LaLC1uYWO9vbtM1y0L1+nAphlxkXhQjnAY6rv9ex/7MWt3jUsv7+xVb5iTMQ/VDNeEpD1xJGZE1oUmISolbMF4kYswnDNJngjCpdB0/TGauuzWZkUrLwwQVjl8Vkmg7WUlJLfllKWtQ8jfv5EhfVkNxru/Xt70evlvJ7nbtgpymFD5ERsfRiLire0+kJN298xIfvvUe9ucF4ElBJJAUnoaQZIXUty/kUh6NZtty6tc/1Gzf41ne+yaJb8mf//M+yd+UqmRYXRqgTusWS6fGUrm0Zjyp2t3cYTTbIJLbEM20i85hpY0flHeoszPNrBK91/YT+R3rQTcU8waRIyhAjGjs0G63b+8wyNsSuKSk8055oU6Ih0UmiTQ0pLRFRRtWIzbqmwpGilaO/9MIrXH//I95/7zovXH2erfEmzXxB8J5O3CrsEbnvNXqQcZn6mrPjYpDxXndmn86+XKj0xBqIs8M5c6OzWCdrRWk7E0/dGNX4NKYODs1CCDUueGY54boW1FiDMZpLHlMmqVUaQj+RfZz8yYYZgwE4M9ZvznNvnv7/qsPNUFfWj+LO/j5Hx0cc7N/mqe0RoyD4kcOR8eLp2iXz2QzvYNYsSGmTL3/5y7zwwgt885tfY3oy5dtff43P/8DnqDf3CNWEFDtu79/h1o2bvPP228znU774Iz/KT3/lp9i7umvclGqMVqUjes6oyUcUMo4M59mzK+17rk8GQ5GWqBbWYzLvUROObN6k9jn+FVhpGdds0oKaTDhGlM0Q0DYi6tna2OLK1h5PX32a4D1PPXWVrm2HpkJDhPqYwclP83iCDYSc+kvErcRiciTlDrw1fR2Pa0ayQZpMyAqCh8WS0DZlBRK8GieiiYkuFx0I7eHNUqdQ/rrs6G/2jzMNeplVzPgeVuI+rkccHh0ym83JKTE/PkG8Y6Me4RSWsxnLxZLlcsH2xibL4Dk5OWFra4uXX36Jnd0NUowsFnNmszmL5OjSMcv5nPFowg98/gd49qmnuXPnNqrKtevXGE1GQzftyXiMd26lHTGc+2n9xd4My5o9Xs1jz0op4JBmK/FPFoNoNtUkcVJaECR7r0ukLlpD4QwSM+1szofvvc9Lr7zK3qu7jKqajfEGrgpEzVy7dYNZszxNq1+tF4987c67Vg8yLuulPux4gg1EGaqDy2eCoNbEtdMWhjLuykRE1Oi2OUObrBRZxJVeFtDkzDJFOl3jzBmNDz1zhwyUmftck8tctPtRcdfTXA80tMdNenfYMRqPcdkyGqKgMdOczOnCmHoyYXYyY7GYm8pRUjY2J5wcHXL9+odMJhPabsnW1iafffVVQhhx0rQ0MSFXn2Zrc5OtzS1EM8E7uq7j+PiY5XJBdpYBGJVOTzln6wieKbTTPkRai7npPYieeqxQiqtMzKfkobKltTVZEV6MPYZkWYy2COfQWSOl4IWNqmJvY4txqIhNy/H+ASe7h4S9PbzYPRRT4ub1a0ybpZG6TiexHmpcWHR4ZjF5kIXlfByCYT/3O/a9xqfeQJz3hUVWOV6jj/YKRkaSqYpMlxQ6aRBPFcwQaDDOzWK5xHurGuyi0iXQlEyncWDMSeGyG0YhfWxfmsJ+nD0v7ldifJmVRpwDNcq49yYyUlc1z+1d5fjkBCemdq0xMz88gjYyOzqha1tS26IxUXnPzs4WKScWy3lpQFSbZPvWNht7AXUeh2lydI01653HbqV4HVtihjCeMJlMLAdfDLt3fu2EOeUtDEZBrRpUi3Sdpo4cW3LXkboOYkSysSX7a9e0DYtmyaJthsKttmmQlBk7z0QCz+zsIU1LXjTE+ZLD/TuMQjXUi7Sx47tvvMGybVA3HnCfj4tyffZa36vy87xF40+8B3HWqpa09ymr65wbOkz3itd9ujLlVNrVQ99Tse0acgLRUNYl0zfIaqvRUKRT9n/KvXxMrubZ7/gw7929LSWzYXPmnR9u66tPPcUHb73DdDple2+XzY0N2rZl/+SmMRFjgjaSYmdgcHBULhTGpcmzT8YbTMZjUgimk1hqLbquI0eb51ja83VdRKpq0ERMyR50ckadYUan/LOzi8JgJEw9G3QAUwXMhqRkTYKj1RYs25amba1MP2VyaZ5UOU/tPJNQs1mNmU/nHN++w4fvvMftm7eYff7zNG2LOMeN27d49/33yWpl0ql3Yh4wk3GZa3o2K3G2Ye/Zz3xS7N0nykD0I+dsPaPUJqqqKuq6Mk3KlPEuDitOTA2z2RRtlkX4ExjSVQOdxMqE1ToORYxtp6XJCFLUDtauyXkZuU/tUAiVdQKLKbG7t8eH3nP71m2efupp3GSTOlQc3NpnXNfEUjMRY0c9dlR1ryGxQeUD48kEFypShqbtaFMe5seJUdjVeVRMBWk08ow2NqkqA4fzYNntoa/WadOrU179DAIwJiufSk22SB5wiJRNtyLHSJtS6YVSUk3qSZ2FTJN6TPDCSIIVkDVm0L759W/Qody8dYud3V1iSlx/931OFnOydyj9sfrx6QMqPw484okxEHelgbBqxgxWnScB7wIOxfsW5yxW7d1LciaJ0MWE4HFVjScwqScsUmSRlqSsdCgdYi3tVayl+kUTf+YeuShn/SDjMjHkZfa9BrqjmLxYViN/OQGNkZODQ5bTOXFjC/HCqK5IpQuZYFyRMKmpg8flDmmXdHiWOAKerqqgrshI0Xo0gplinbVVhOQdzgv1uEJKX1NXXdwP8i7asK6bCgYYQrNhD+RsNR6tPeixaVjOT4ixQbGsRc6J2LZ4oA4BUoePCW1burYld3DSRFIQ3nnvPbau7DKNLddufFTA3YCqt5DzkipAF2EJF6Urz773sNf9cadfP/UG4jw3a/21LErbdTTLFqmltDWrWSw9OTqyVEQZgSg5djRtB5oI2ei+latogKyVVXaitNqay5wpTVCssyFwT6/hvAt8GUt+P3Cy//uy1XjDsdfAW9OBSexub9G1S6rgGFUwPb7JU3sTmtjgJJG0pa4Up0LuOoIG8iySFnOODg5YzGaM6glXrzzN3t4ek91tFCHFZP0zREwItzZOxcZWwFU1OEy4R3yBFSyEMwakeXO69t16RqSqScaRMiQrstIukrtoSldtR2pbchupoqlnpekJTjDhGE0Ql4ykY2t7Ay+O2axDJXHSzDnOHVEExoGYM/vzE44kMk3KYZNopUaKSgRlVh+n93Axxnb3uIyOZr+PP1EexLngzRoOkLpM7BLJJbyASAKJhYgjLFMygZGsRpCKLdK0LBYtqRWLKXAr/ll21u+7NMARdedeyMeRprrfuNcNsz7OXzVMa9I5wTvBqbKzucnh/j4boxFX9/ZYnhxycrQJVcB5aOMSp5jgK4KPif0b1zm4cZ35wT6xawgucLKxzf7WNlu7u6gLpaBNqccjqsmYemdMvTEh+D2EqlRgmlBMr3YliknduaLvuQ4Qn5rbZD/at7ezWluvlgCJWUhtJi0bZkcnaLJuXnnRkZuGtFyyPR6xPd4wObquI4plslrv0OBQFWKntJpZdC3HTUcnkLxHcAQUY2xcTkLlLE/lMgK4j3PlP29ctpZjfTwRBuK8oQMzgdKbIqGVybWnbBqEzlvr9GXboG2D5kjTtoZ8q9I2yVqT5bMXvYe/1tJv57i+l0GdP4lx7oVeIx35on3hnWdU1xweHPLS089y9coVbl1/j1sf3WDn6auIE7q2xSEEEbrlkvc++ICPPniP5viQMZmJ94wq8PMZ05MT4sEdfLDUZRsjUTNSeTaf2ePqc88QngW2Mn6yidTmPeS1jGVPP7vHt1v/SqcduEKWImdEYbFYMF/MIXiarjF8pIs4Efa2dxj7ygSKS3p71kUQh69GLLtIJ1J6nWS6vpcn5XzlwrN46HE2jLgMn+XsuEx156OMJ8ZAXPSVhbslymwlKo1zROzGbRpyamnbBk3Z2qCX+b5MXfypc3nMCPKDxJiX2x8reB8jDAkWfvWNXZ966mlLfzrr35naCA5yF6lHYxYnU669+xZ33n+fOJ+xFTxV8ARNVM4x9gEc1LnDtQ3ee8alQGSxmLF874jbh/ukZcveZ37ApO1EEF/jHKek0Vbf//Rr9lPe1xVuMYQfVrsNWYlty2w6pR6NmeUl05Ml0/mSLmVGviIUDUxr5hxJztMla0un4lh0kabrSNI3qHHWDbzv3zncLKf/+eDX5uHT1v3nH+S9R71XnxgDMQw5BVkNRKXeSKSULOZGjJQjQggeNxrRtRmfE+INRNsabRBaR5x1uJTRFK23RtlPPsdLOHUqn5DH8FAXWctSndUyPlhPysPDQ57a3mWyMWE2mzEejQjB4xGa5ZKsmZPFEe+89TYfvfc2465lw1lRk9ME4hCndKkdUn5eI0FGeCelbiJRCXSzGXc+eJ/kap73FSRBi9y+ugBV6WNSBGpK1bexLPNapoO+HsZSlr0QTNRME1ua5Yz5/Bg0IgG6WUcbO5oYWS7tb5c7dja2aLtu0MQUAZVEEyOztqWNCZxDpDXRGy+MReiy6ZIW7etLTf95uNnZe+lRF4b7kaoex0L2RBgIK5AZ/lH+0FNuRS79GJw40EztA5ujCVujCRv1mKbk4jUnKucJPlCNNmCeOVmkU6Ci0vdSWMXDn5QxuAjcfLBiLx2sqAPGdU0Qjxfrj/HsM8+AwtHhIS89c4XFfE6zWJBzYtkseeN7b3D92nWCJkZincqz89ZXs/KkumYZEzlBFSMVuRQ7gVeriXFOqDTQzJZ8+NabaITPfvHLdF0HqUNTgDwaWv1pUQgTESSvGwhsURj6lFgFZy5Mya50ooqpAVGWbcO8WTBvGxZtw3y5hHaJtlOj1Ys3NSsRknO0mpjHaK0PcFYM1kVGPpjxTAmaZRGKefDr+CjjbK3NOtvyk8C/4AkxEHeNc4DknK1Zb/AjnCrb4w2ubG2xu7HFRj0izea0XUKy4r0bPPAYjdyjetrg2LK1ygQ8icM562YVXICYGVUVT129wuZkg8M7d9gej0xVSqFZLHFeuH3zFh++/wGp65hsbpr0vXMs8SxjS5BMkJYsnjZnUjOncjBqI5uhYuIraheQDL5vwhPnfPT2W1x59ln2XvksXRFXVU1I9qgrBvkC45hLr8/eo0ulIUyMlt5MbTdI3Hexo1NTCutipEuJzcmIOmgBnoWEsIiZhSbmObGIyfCHNfC07To2q4qRD+RQ08bW+BufQv7DxzmePAOhYEuKVejbQmnycE3XsTEe43xgczJid2ubcV1bty1VAuZ1e3E4F8gKMUVi6kjZKjlTzqWZjJQ9f7LMtQep3ejHXQZM+9etl6MoaMrsbm+jSbl96xYvXH2al198AUkdm5MJ3fSEk5MT9m/fslqEekzXJkKoaRVyTFRVxed/+Au8+Mor+MmEk6bh6HifjbpieTzl1vvXuHHnkJFEKjy1c0jbgRckw7V33mbj6jO4emzZCDXBHlnjrK/DxUKPRRQZ/7J9TGYgUuxMKTtnUmtaECknupyJZJrYIU7Y2d0hpAZNybqpJVhEZaFmJJYpk8VZGwO1s4gxmaivrwm+QlKiSEXzSRmJT9JzvWg8MQZimCY1OrSIRYRZQXoB2gxdMiXrUV0zqUeoJhZNAw5Go5pQeZwEa6CSEzEnGu1YpMQiG0nKCofzfd39iy7egxqTh7kJ+pvnbIGP/VG4os7hfIXiLINRVcxmM+rNTV546XmqjZpm0VGNxtCcsDxagtg85ZhxCl3uaFNkqzax2udeeJEf+eKX2dq7wiJ2tF3L1at7pGXLO9/5Hq9/4+t08zmH+3eYzqZ4VUJhtO3f+Ihnb9/k6vYWPgVyNuOBgIq5/aqCtxQMfYfwXsU650TKHZkWzUtinJPSgpyWaGrJpWVi7BIniwWLdsH2eMKkqvFAmxumcclShaWIMS5TLOxtsS5e3pOS4SJNNGPggkdKc6BTDLQHHPdLWT9uktPjGE+MgTg7FCyfbp1UyKJksYKdYQNMRCZ2Dbk1AA7n6JoFTdMhYQTB40YVy6ZhqUqmEHlUV0rLazHfg6amHug73c8zuOyQImrirXN3GztGODzC3s42P/KFH6SeVNyZHrK5OYbgCJMRo8mEOlQ4bF5ztPqGIAnXOZYHd/jqv/4tPnzjDXZ3r+DrEfV4wvbODpqVozt3yG3HZDxiOnK4pZK7ltgBKTM9PmL/1kc8/dnPlPAj4nxAXCr1FqVvRfm/dYXqS7lNFEZTC7khpyW5m6FximqLD7ZwpKS0bWI6X5ByYntzRJUjdAkpHbWWgHq79XXZEbxHNdMp+BBMKj9hfUuLQK9zDqd5aIXwcYyPy0u9bHr0vPHEGgiKRkOflV7PbPTZzi5GNEc0Ge8hJms35uuKWjwJj5qUkgmbFhUp1dNY6IVn8DGQou733qVLgDEWaJciPmXq8ZidrS2eurLH3t6ONcB11ti3i5GMEoIvnbkb6lBZYZNExlVgxzvGmpH5MbP3F8zffxfEEXG0CRAh54iISfqJZkbem7GpAlKNaBycTGfMmw636QEPWn6vXUnLauZS9ZmtglOtZDunRI6ZHDMaE9olclRSFFLMdG2kWS5Jy5bd0ZjN4ImLBdplqzp1AWJnKmPekUMiqtHPJWeqElZ2kqyy02di0lW67BwexEXX5PvNkXkcx3xiDYToegGVXTRXmHVGLXYGWKUFghJESOogY92kHTQxWp9N1YJm9Bf70+PiPdrweFch2lhev6rY3N6iazqcF64+fZWubWiWEVUYVSO2Njc5ripyNJ3OceXYrBxbHrYqZXtjG1LGJVvhGxyxCqAZFyoDBtuWlDMVghvX+NGIecZk7aoKFcEqvgOqoWAQOgCElKbJfaq2r6fpO0Gl3KGpQ4sGROwibdfQdC3zZsly2TAmsDvegA6Wy4aqhFzBebwktIts+ornXnwJVeWjO/vsT4+osjKqapoc7ZyyAdui6wq7F6cUP03hweMYT4SBuIuxyEABspRkn5osbmkqCHeriZw7au+th0MwzKJpOzLQdC1tgjZ2q5ge4bKe3icRL15U+3+v42qJk1d6GSb1nnIihIrlcsnm5gZgWZyUTO05jCqeff45Utux/9EtYtPiPEy8Y6sK7Iw8T21tQEymCamZ5CfUkx2UTDUZ0zQNxyfHLJuWtou0HqZtxzQq288/zbOvvMJ4a5vGBQRrmGMuWy8hh6U7dWUYpOdD5GR/JyXHhKZkpdyaaVLiJEVOupau7dgMI1wSumicBslW8emdUDtHmxJBAk9vbHH16hV2NjZ4431Y5lSaOQsxKS6XgsCetHWBS3kZGvVltju77eO8xx4mhHkiDMTFY6W3mLWksZLlxlOMpmIkDl/VhKpm3rTGhwDEO5Iqy7ZjvlySUgaxm9Z0H/Kpm+FBaLD3e3jP8vQv9U3P1Cjc+3irlG1WLerWyvHxMUeHh4SdHba2N0tJtIGxPgRSatnc2eHFz7yCDxUnd+4g7ZwF1rPSZ8UvltTirA4CIeZMs1gYVtOZDsSyU6IGljlzEjvaUcXVV1/m81/+cZ579VW0ChbbI0jfwKXM9roHocnCCOu8Gw2ELP09NSU0moRcFyPTZsGd5Zw7ixkxKdvVmNguoeAKXqx5swhUYqB2bJbM5yc8dXWP7a0Ny+Ys57Q5EUUt7SlawMzU+zhl0s+/9heN9et9Hk1//TqvA9APut/HPZ54A2Ekmgxi5d+JTOWF8ahiY1SBVISqKp2dI22TwAkbdcAHx6JdsIitdWfqsQfNIPme+MP3Y5xHkLn4JjLNxxRbQmWtAqfHM44Pj3lqdw8QUhdp2w4RGE3GNAIuQ6UbXHnpOfae2iUt5mi3hGbJvGtM9FcczlraIlHBWeeqLkUikMWRg8ONttjd3mL7pZd47tVX2XvhefJ4RBQlS8TpEtEal62UOksJ9bUPJcw7oOvI0cRnYtcR24a2WdIs5iwWU05mUw6bGTfnB+yfHLGVanxlmZN6PMZ5JTftqjmwy6hT2rbl4GifycaIRRLaFHEhWOpboUmZqMk0QsS8mo97nMfA/H6OJ89A9DdR0SMcqK85940grON3UYYCpW1amiaybFvarIh4Uunu3cSWmDqgXjuIDsda/yc8HqT5QS76vcDJCyngw19Fji8L2Vkh2/H8hEVnStVdinRdZDyeUNe10agBvzFmsrdFXDZ08yXatcTFlHZ6zPF0AQkkC2hk7ITaexNV8WMmm1vsXn2KerJBVY+otncYPfUU451tcl3RZEEqKx7rO3lbiDhEGgMY2XsLMUW6mEgxkQr/QbuO1DW07YKT2ZTbR8fsn8w4bhaMq4qtzU2mTYNi8vZtm61XqyjqHBIcBGERW24d7NNpRZOE6ALLGJnlSIcSEaQUu1069nyA63r2tbOewP3ut4+bn/PEGIhTE1Ewhz7ONr5+xuFwKDEmmmVHBSDJPIcu0WZoknV+7qZW7Rejx2lNvuQzex6X/p7nesFnH3Q45+6571PHL79zzqbw5IR52zFLkcPZglu3j9iZ7OCC0nWJ8cjyxaEeWTbDO6qqxksgtZncJWIzIzULUtMiWWymRXE54XMm5oxIYLK9zebuLr4OVHWNTCbk8Yg02rDUomC1ECKlP+cwaYY7FFKXpTTtJ8cldAtc2yDNgrRYMF/OOFnMuDNd8NHxnA8PZtw5WRBTZu5aFgHyyARqpKqg6qztHjAJFZKFTIeK0KTI0jkaItOmYx7XPCEckg0Af5S1fJ0mfb/t4G6adf/e4zIIl8VEnggDcXpSdOBPUlq5S2m75PEEMCm6rLQxszkZk3OLdg1NG1kmNdXqNpUGOR6RsMaSS+eew3njcVTg3W/0GEFvIB5sf0Xy3QWarJy0kVGnTBcdi2VEJCECXZuoa4erPM57wqgupLIJmiucBKrgjaJePoMaD8WZMgvLxYLUGQgaQljF0lWF2xjTGRuqhHFWF2Kl+TbnfYEcycr4NbXGlOw6UjdH2wUsG3Qxo5tOmU4X7J8suXYw5YPDKbenDYs2U7mKVoQPj/aZACMH89jRAcfLJQCTrQ1cSia0q0IUTw6OViJLbehIJLEGwgai9v97NHf/MgS7dezhPNzion3243GHJE+EgThlRfs/C1vQlUl12XIaIuC9tY4DwYeagJAWLW2MdMVAZBG6FGm7jphiqeID6GnW5x//Uc//suPRVopyYxVfImtGxbPsIrPFkmXXsWgaPMrm5njQ9qTv0VmAWnGCD2OCr6nqmroKhGBGoeed9N9ttG0iteS8VvFo1yB6d6bMRcr/DSfI2fRAcy9Pn5UcTVk7dRGikjulazoW84bpYsmdkxkf3Tnk+sEhhycz2jYiWPPh5AMH8yUHbcvuaMQsQ+wiWRxeHKlNdBk68SQnbEw2CM6hboYLVeFhrM/koxuHjwtL+LgxikcyECKyB/zfgC9js/i/B14H/ivgVeAd4D9S1YOy/d8F/ga2TP8tVf3vL3mcM/+GvtrKGtNausw5R1WZeK2IEEKgS4n5sqHtTFQmpkQWRxJZSZp9QnUWDzPWXdOHuxmsq3UIFcuuZeE9y6bheDZlezwyqfrYUceEH5nEv/0EvPdUVYULFT54625deXC9CXDDc+OrEdR9YxodaGxWa2FK1HeNDMha24IcS9FVLMahI3YRFzNtk5jNW45PZtw6OOLDW3e4dvsOt4+Pmc0aiBCqmlAHkoOlRmJSptM5XuaM6xF72ztoSXFG9XQoYVRTbW6yWCxoU6bprA3hGrr1J3o8qgfxnwH/XFX/lyJSAxvA/wn4F6r690Xk7wB/B/jbIvJF4K8BXwJeBH5dRH5IVS/v07PGcKRfiWwtc85Rj0Zsb20zGdVoalFg3jQcTWc0rakdxdQhoSInSKV4J2dlvT3Dg45HMTCXLc56lDRWX+TUtC2Nc0znM27t78PVPeo6gCj1OFppM9XgPTjnkCqA96hzVu4tgogrVHeH+EBPPRWvOB+RwnzMRQ5//QqfKlnGuA85RWNI5sJtKNmK1HWGf7Qdi0XDdL7g4GTKjf3b3Nzf5+DkmEXTkFMmSEVVVfhRTRsji7ykK0yL1LZMENrpDIewtbWJq2oWTUsznXFzNmfZdcy7jlazfcfhfvj0LR6f5IL20AZCRHaAfxf43wKoagu0IvKLwM+Vzf4h8BvA3wZ+EfhHqtoAb4vIG8DPAL9zyeMBK6LKyoktoYT3VMFWvXEpNupiYpmiMSZLObELgmIall2KAKfi+0/rSCldOje+Pvqt265Do7BwjoOTI2oHm1sTJssFMKLtWnxnD1gAVAXxrjChHfheT9KjztShSjw3HMtSiJBiJqUeLcoMeg661pBX1loZarZsRduSOvMcUiy/28hiNmc2PeHw8IDrN65z7fo1DqYzuhgRhcp7xnWNH1V0Cou2ZRY7E6oXh6trImraEF0iVTUbvqLFcdyYDKF4U5Yyw1i+SD97PUD5mF2Ky/Ic7icM83EdGx7Ng/g8cAv4f4jIjwN/CPwnwHOqeh1AVa+LyLNl+5eA3137/AfltbuGiPwS8EtgLu/5o0iQZ0g5gveIc9bPIUXCWgPflPtmbQb6NW1LlwqBxvtSi/DpHI/kOWBEKcTSh4nMrFtyvAiMgmfj8JCRQHBC27aMdWMQ3kml2ZBguE92RbrOuWIgADEi2nADr8+iJJS+huK0cYDiSdgfpC5aqXZnJds5tmiMdMsl8+mMw4MDjo4O2b9zi2sfXWM6n5FiwqtQ4/Ejz2RU0Tpoc7YeJ5i+QxJBpEI0I0lxouyfzDlZdiTNJAIa+u4axZj14ODDXrQHGJetsXkcxuFhQtRHMRAB+ArwH6vq74nIf4aFExeN887u3G+sqv8A+AcAdV3rxZsaGNHPmxPr2pRiIlSOlCPL0tili5GUIil1qEIVanxdDStZlx4o0vm+jfuj1nrqtwHw5s4nVWLKLJqGEx84nh6zM6rZnIwszVi2LZziwlBNaN8xW3vwxxWIcZ1Z2DNP7/4ZKN85D+cznH/OpTdFS9c2xK4ltg3NfM7x4RHzkxMODg+5c2efg8N9TmZTxAmTUW2FV1Zza7U3XWuGJkYrElM7V+NYOLwAwbNMmWUyHU1VS5AjPfC6BlIPhu9PLhrxKAbiA+ADVf298u//GjMQN0TkheI9vADcXNv+lbXPvwxcu+zB7FqlYt2L+6fQo+HeO6oqMBoFqsrhXSBGAydzVmsJVxrI1FWgqjaJ2AMWUqZLq5XtcY/LpqIelK9/LxYla6t5f/SEqSzNAOc89YljUpob7z39lGEGMZELKSknJaeIuM6Sv6Ko60Aha68j2SteZlTMmKjcbRz64ist6lBmKKzXReoaYttYX88uMp/OmJ0ccbh/h+PDIw4O73B4dEDTLZls1IzqCoen9iPEeZqupek6lq2iMRoDszzqTq2dgYhYwJNzMSla9DB7TKWfJcGpsXL7b7EWbDzUeBz6IZcB0x+ETHfZ4z60gVDVj0TkfRH5YVV9HfgF4Fvl568Df7/8/pXykX8C/Bci8p9iIOUXgN9/gCNS/D9EHKjgUHLuGAXP5qhmc1xRBxhVniCBnJTlYlnAyYirAr6qCMFWHz/knd1pVuanbDw47tAb0dNDyXROSRppmxldbnDOMdrZZq9ZIvMpO2JhnfoKGSupbW1vmhCpUIlktZVbxQEBoe/7aQ98Lm3vcmdqUbVYeJNyRlIHmHpXjNYqL87nLGdTZvM57bLh5OiYg4N9Du8ccHJ0zGJ+Qhh5xntbbG2NGTlPUE/lArNly4xCYzF+lbFhNBUgNA8FYaKYkfOYkZNcsIUV1tA7RW7tdenf+z4NKRm378d41CzGfwz8v0oG4y3gf4fN7T8Wkb8BvAf8VQBV/aaI/GPMgETgbz5IBmMFTZbmJYW8IijjUc3O5iab44qxF0bBkZMpLJvEWBxyoypWpKXZYvNcOoCXc3zE6XiwcRlW5kPsldN5+2L2yqpuZlVpU+QoZ9zBIZPtbXaOj6hHFaOqxotjFEbkLqLeI76j1Nej6siu7N8BGhAJmLyO1XRrKuXZyVZpl7MJvqQEuTUiVNfRzuc0iznNbEbXLJmenDA9PuH2zVscHR0xPTnBIWxPxmxd3Wa0s8HmpGKUhSoLkoUgM7oulQpPNZ2I1K/7CUpLnn5m+g5ea/gjQ1JT7rVEfLLg4KdlPJKBUNWvAn/6nLd+4YLtfxn45Yc7mpz+SyDljBNHXY/Z3tlhc1QTcgR1RM0mG+cESZaVNzTa6nezpAHU/Pg0gr5f4yJXtgBxWOYmO+G4WfDehx9QV8LGyERiHMJ4HPGtgX1eFa3UhGh9QH3vMjvw0gtB2dwmLWXY5kmQM5129u+us3Z42cKIk+NjZtNjZkeHtIsFx0fHHB0dcXR4iMZEHQK7u7vs7u2wtbvNaFIzGnnqBNIqTdNyvGgGYZw2dwVctfXAQoXeYK7m5txn9JxbQO799p+I8UQwKe8aqmgBlZxz7Ozusre3R0gd0uaSojMZOoAQvNX2w8CpV1Y8iMd99S+r2/Ao40H2e14M7JwbGvoezqe8/cH7eCfkz2T0acH5mpjBTyZUqUarCqkzVNkyRlI6coeMShgYh5qs0CqVPiNkRaQr2EZH1zWcHB9xdOeQw6M7LOZTZocHzKdT5vM5bdvhvTDZ3OKpp5/myt4eW1tbbO1s4byQc4csWnKKLLNhFm3saDUSc6LLyeonxK2ua/E2z7cM/YSe++ep+XvQca/74CwB7uwx7keM+6QEap4YA9Gn23p0fYi0nWN7a5u9vT2Whweoj1gzJGsFpyjj8Zhl26E5WayKEWF6ZD2fuZAPekPcL1S4rFbEReNeBTwPO6wxkHUfiyoczed87+23aRYNi2XDC0+37O10bGZjNuqoJrcdMQRCSQ8778AV/oCIMSj7hrwmGYXkTJsWtMsli9mck+NDDu7sc3xwyMn0mOViSjdfIGRGo5qt7U22trbY2dnh6tWrbGxsMKkn1OMa54XlfErqMsvU0i6X1rg5RpaxI4uaMFDuuVsrIt1l5/hxjosKri57zPV+nqf4Iw+wj0cdT4SBWJFqzEhohr4Nu02YxZninInWirWYWzZLvA/U9YguJmKOBIzw07Ga9Mc5yfcyMI/jwX6cK4ZiStK+gL5NE3nnvfeMe/DiMc88/Qx7u3uMNydsbm4xHo8J3hEqT1XV1NWolEEX0RftoQ4dQMKcIrPZEQcHdzg6OGR6cszJ8RGzkxkpm7ewMZ6wu73DZHuT0WTM1vYW25tbbG/vMBqN8CJFnBhyapmfzGhixyJ2LFNHLF2vYs6lgMyVlKqYt3kf5+HTOu7lRTw89X41LvP5J8JA3G8s5gu6tmNjc4Pj5cyotk2DAvVohPOl01NVE7wnqqlPfdK1GI/joj7OIVhvTk8pZxZh2Sx4/6NrHM7nbH90jb3tHa7s7nJl74o9rF4IlRndUT2i8h6n1g4PNa/Oi3EOmqZhPp9zMj3g+PiI2WxGTi2aE14cO9tb7O7tsb21y9bGFhsbG4w3NxhvTBhNRozHY6qqAk3k1FnKtSD6KZnB73IaDIN4DyWDMgCRT6hxuN/4pO6jJ9JAWEJCBs+xvxF3ntpjNBoxb2fMl0tizqYsRFnZe5agqvXR6Buw9O3fvm/f6PszeuFfpwXFd47ReEyMHdPFgqZtOTo64uaNm4wnY+qqJlQO7z0hBKoQmIRATQ8cu9IZ3LgRKSXarqXt5qRsCavJuGZ7a48rO7vs7u2xs3uFejShqkdsbG5SjUb4KiC1yeC54C0D0po30l/DGE1EpsuRpLmQuu1HL3kxV8yH/3FcNJ44A9EzfFUTSEbUwKjZbM5icxNcTadLThaRlJRQlca1ztOkRKeZKNbxO6XSzm3tfrqo5v6y5KV7vXZRXf/3zauwllvWz1SEycaE7fEIp5mu6WgWS9quZZkzTbss9S4mUS8CgjIJgYnzODFykVMhOF/Uoz2+qqgmY3Y3N9jd2WF7e4cre3tMxmPGGxPzGCYbuBCQqiJ7KQzXjEqmRUFlRblPGY1Gl08xQid4N0aBJs5Ydh3RB3pBIXGQU88KWaV/e9mAj2tReNRr/GnxNJ84A1HS1QwCL9l6Mk6nc267A8iJNmZStrjaizOFImBjMiGhtE1DKv0bM+Xhv0SYsY4vfJpLxC87lEzOVmehQPCBZ64+xVM7O4xDxexkys07+8zmi9JjJA9NdOlxASCLEZ8Q8L5mPJmwtbHJztYu23tXmGxtsLW9ze7ODhubG9T1yPqGFuKaryukCqgT1Al+VCGaCjW+xXmPJEdOcajXaDvLYNS+otaKZjllsWjockJCwHm7UZxzdE1bWur137v8LguDfAKX8qwYzP3UyP5HA/EIQ3vUSU0MJcaO2XyG00zlHJ32cvh28asq4IBqVBNVaWI0CvajnsPauN8F/bRc8FNjoEFnsjMC8kY94rkrT/HiM88RnGP/+JAbt+9wcHhgaciuK53PDSiugmdUV4xGNRuTDXY2d9jbvcLOzjZ7m3uMtrYgOOq6pq5rayYcAq5U37oQ6EQhOMR5UCHlhCDG0cxK1y7JbUNaLmmXC+aLKYvFDB8cI+fpDk5omiUpJ7x3hLpi3rRnvuqnC/85b6yf46mitu/jeT9xBmLFqLSSmpysmWvTtmxNJoh35C4hDrwX0xrwUpSKEsEHvNxf/OEy8l4X5bnPrhaXHeetLhflzx/HUISsQhIjQ+WUiW3EqzB2gd2dHZ566mlefHHJ0fGxUaFLUxwt1jcEx2RjwtbWFpubW2xOtpiMN6iqgJdAqEdoAYllUKyyStq6rk1FOkey9wjOop6uBQWfFU1CjAltO1K7pGuWdO2SqnbUYczRnWMOjw5YNkuyqvEvujikr3PK53iHfZNeGf79cYyz1+pBaynOu86ftOf6BBqI0rwXsDZZhid4sV7fo8mYaTclpoYutaTYMmZM7QMbG2O8rzg8mZLLPF/2YTt7se9HfFnf7l7G5qwhuReP4mENw0XnIKWeIhcdhKZpmS+WJq6TMoKjCjXPbm6xu71DE7vSZEdKX1RHVVtGox7VVufia2tUU/bvfIU61oRqnQnROEGdI4lDgilq9whBYARdQ4qdsS/bSFw2LOemCxE1srm7xWyZOJ6d0MSIOiHUNYhV5lrtgpgEnioh2K2eyuuqJpL7cSKUdyuhPRxf5pMoILxoPHEGoh+KgFiRUFaIMdO20W5Q561/QoyMfMAp7Gxt8fSVKxwdT4ltd5pGu0ZC4czr/bgfgers++dd7AfhXahaCfN5Rudhxj0rCdU4EW00eb7j+YxZ27AdOzwjE+HxYyrGpZ9nsPjdCfVoPDzwiFXRivPGWVAHznQ6DNW0TIkrD2tWUNH+ZVRL6b6zykujTSc0KstFw6JpSc7hJhNcCNy6cY1OYLQ5YeI8tXckgSZn5ouG5XKJqJKzZWk0Z2vUK0LXdQPB8uNOY5x3/S402hfcN5f57McxniADUVZclQIu2Q2n6ki5p06bQMx8YaCaABvjMVf3rrC1ucmorg0FT+kuYOpe1n79tfvRqB9lle/3f7Zy73HHoHacwiRVq2pUPCnD8XTG4fSEw8WMrXaLKo0ZaUZKBkOd4LwnC2Tn8HWNc64QlEzLEudNbhJnRVzGYhsMRT6l76dY9wk1tao1w5DUfueYaJcdWYVqMiE6uH7rFssU2X36KvvXb+PahK8r/HjEi1evMh5v8r3XX2f/1j6TyciyJuMxXUrsHx32VMv+FNYm5+75GtKh99nu7BzftZ/7PNQPcp0vKzTzqOOJMRCydpUUCyesZsjk09ULXc4cT+dM51NSsq7Uu7s7bGyOEbGqz42NMVXwSIz3DT3v5ebdy9u47D6loOxnw4yeCfhxD0Pwew6EtQqYdw1HiwUH0ykbmxuMNsaMmFB5AxRxalkCMT0IcdYMWZxHimdgZEpfviPgw2AcVCy0KDOAaMYpqGb6umrVSCISNZo6lCbqUY2yxeKoYf/ohBt3DrnyzHN02dPFj0g50iw68mJBxvHyZ7fZ3t5m5Gp++Ie+wGc++xmuXr3Kh9eu8Vu/89u8/+EHp5wHPTMvT8r4uEORJ8ZArF9Kr3FQOzBj4dAstE1kIUtrC+dMjbkajemSUo8rNrd3mC86i7v1wSZ3HQc4jwt/GdR5va7fOTcYh3sd7+z+zzvnB8Uo+s/1hdBJTa8TD12OzBczFrNjTo4C2+OK0aRiu7KuI1JymzKcvwccTjxlUlf4EIIXKf003BoZrT/v/u+i7yGueBKJTKntUMXXAacbLDWyP1vy9rXb+PEm1Jsc7B9w0iyZN0valGhipOlarl69wg989lWe3nuaV195mY3NMa6q+Oij66S2s0p11aFwz9FXfq7N09pfZYYvNb+Pa1w2k3FZw/AwBuTJMRDD/CiOwrNHUDFZtJScofBdonIVVZVAHDHbgxMznEwXpIJdWA+IFbCYzpGcu8hLuJeRuN/Duv7+RUbg7Dh7rMcRgyrm3CtWw5BVUS/kpLRdQ9fOiXGTtp3Ttkti3CjYgbEkXR8yqEBpVGQehYm++nLBnGBSb1gcaASl1dxk1KThFERdMQrWvwLnySGgVMybJTeOTnjte+/w9rsfMtrc4rsf3KRJiYPZjEWzwBW5/p/8yk/ws3/mZ9nd2qUm4DWxf3CbP/z613j9jTc4PDgY6kacmDR/Idecnp819+KTMA2PEp5+XOPJMRDllu4tuWWpSst4MRe5f+jEOUJn/TK6trXmqzGRuE3btPY56a/65duhnf17/d8PGmKcZxzulUZ9/KPvW6FGUCoFWzlbvUOMkRybtbTy2ncATOjPDR5Fj/bJ8P/VtroWLqkU1WgAsX4mMUe0PI2iGRHFO4+rKlQSs27B7ZNjXn/zTV5/+20OTqbIbE7TJSZbm2xsbbFol0y2tvmZn/4z/MVf+Is8/dSztPMlIxyVcxweHvD+u+9x46PrplkpKwMmGaKUrMcaNNHbi0+r0tgnMZ4IA3EaGCxpbdff4quHK6tieoeW1kopMVvM8T7QpsSibVjM53SxK0Sq06vGZc7jMvyI+43zUpv3y27cL8tyL+n+i1xUSxhrLzZF0W61/QVvJLSUS+8QK27z3lt4VFKWUh74dSzlLoN6wfca3HvnyjV1SM7mOYjYjyrzZcN7167x7vXrtEAUq/PoVNmdTPjBH/g8+buZP/Ozf44/92d+lheff5HgA3WGvGiYnxzz0bUPWcymSM5UwRGTFiNnc9M3XrSTpvRqHYCvojh14RQ/1DjP47yfl3rRftbH4/QonggD0Q8zDquLNkS0BZG2m9XjPEPI0MWIeE+zXBBTJMVMV1rLr1/x89z2+6Whzm5zGXLU2dDirGE4L0tyUdjyqHHpqlgLckpIMJXvlC0jMN7YQPw6TlIyLCmh4skx4te+x3m4iip3GYh+2/47rBsIcKY6pSY03DUtd+4c8Na779GJMN7d5eZ0Bt6TRVl0kevXb/Bjf+rH+YWf+wvsbl8hth3jsSfFyHe/9U2+8+1v8fqbbzA7OaYK1oxXnYU21u45W8ZEV7DlXTMr6/fdo4+zYenDeIvn3TePG7R8IgzE6qEyD2HtHaC//2yifaEMd401QxHnaLu2yN5bKXCXon1G3F3Hsf1dfmLPrtz3Ayn793qw8jLg6Flm5uNaMbSvQ+nnq7aGtUnVQjbvTnUV1xKzu9LHNGvGXWBEVzf9Gfrw2t93eVL0Aimrz5+cnPDOu+/xwbWPOIkdNw4OiP02LnB0ssCp46/8yJ/i5Zc+S7tckpqOw9v7vP/m2/yb3/19vvfG92iScV9qX+GCWlm4igncKFj7r/XQaNWhNQ+k/I+nwdI6eH2/BeiTHk+EgVgN4XRiyiy+cz1Dz1a/HCPBB9NLVCXlTMyJmNNQwUkRE+EBJv0yF2h9NbgfOeZ+K8C9jvt4bpZyTDVMRsp+m65l0SyJOZmH4dyKCems3FtcX9V5cefpcqaDIVg3inetdGsgoRQANMbInTt3uHHjFlvbOxzcvs1ssURCjaqsqNkZlsuWZtmS2o733n6L1/74q3z3tW9zsn+A5oiQcbLKWAhYmXsBTt3a8Vf/L33bpEe/Vh7GJzHul734JLgQT5iBODvsogli8mcIbdMiTtnc3KRNHU3TrFYCNUORP6FrfK+Q5byH6mG5FQ8z+iMVserBsfYh0Cwb7uzf4fm9HdLuaQxiOG9dSf3eO2V7GoS9kIC2tnlKidR1LJfLUtxVA57YJcQFy0T12RSUHCPf+da3qCvP8cEdXv/mN7l1/Rp50VDjsJL2VdYmKzjNa6nUjJfTPd2j9luvz9afvPFEGYhT7taacRBn+gSLdolX2Nyc4KoiNCLOioGU4ceoxcNeP5HzvohHca9xWXru2WNd+v2yPPZrZSZTVxUpR46OTti/c8wzVxpiG0ldJPtE9olEhGAdJ/q0Zj+vDr8GOZRwKq17DiW0UB3yiL0vqGqFVTlFjo+PaGPChRHH0xlvvvUW064lxohUMuBR4iCmlu+8/hoH+zeYH5+QlkuCZEQyaMZ7AekB7OI99GGDlFSsyimm5JD0LhiKlpJ4KeHsebP8MF7degj5OPYHj3dheSIMxHmrrROxNFmJi1WULNlYfXVFVCVmSGpYfRczXeo7OhX8+vu0MNzrpujf78d66HEW3LxoXPSZu7cpD3B5eNrY4SYjXGUanoeHcw4PT9iabLO5scW4sl4Zfe2GOockjAiVVy55SnkVipS000DOignvLd0p5cEz4Vslpo7cdsxnJ7RdSzUacTxdcP3gDtNuSVSrHs0kqmBs06qqcKljsTzi9u2GKgs+ZbKCd2J+RhArMU+RJiacWrhRoAwr/dNAzJYJQ5WAZXFMvk5NIf2C++V+ocD9rvHZ19av72XDiI8Ln3giDMSFY23OYhfxzuFDGNzhtm3NPZZeaTkhpZpw7fmwXX2M7nw/Lnux75c9uSjj8qgrTs4G1FWhQjuhWXYcHByxOZ6wubnJaDwiOEGdkqN1/DZauLEpVT1OSqeyMyHFeefWf4X1FHUucnKTjQnvvf0ur732Gs+/9CJXXnyOP/jqH0GzRLAHvus6UtdA6nApEhvFizfJOzE+TI+r9Mcy7MGUrwrHC7BsRnBr6fLSfEfIfFJdW++VQr8XRvVxjk9/3/syhtWwnyA99eaAfrdty3R6YpV8ZdsUU7kBVx9d//j9VuT18SiGZD2DsX7M9XTn+s9A/PoYborzjJXpcyZTrK5HqCrT6Yyj4yNOTk5om4acrQlyzpFUfmLsyk+06ktdNevNazL46zhGPwwTKO5/6QJeVRUnxyf8xm/+Bl/9+te4uneFL3z+BxhX9UrRSku5ds5oTDhVHA7JQuWsf2ddV1R1sAKy/nsriBoF3JUfkRWvw4mxOF3/2sD5WIHjPYjZZ2lW3+TRFpmL7oPLeA9/oj2IIdUmJQbMq/hVxJBsH0w1KseCN+RsTMEicqo5D0BaznoKh3jUuP4ynz0LUN6L2LQ+LhOGPOw4tY9CWY8543woupKQtaWLLbFriE1LHlv1plGj1ZrkkHBOUJJpSxQKvGYjOlFKuvvvPXhAsvIi+pU9YbUht27d4u133ma6mPE7v/u7qBMWJzO8ilWSNh2SMi4LPsG4Dox9wCsEL1SFTq89VqHWGUzAMCtlUBURVh7Gam56nKvAoaqGaQhr9x6nfj8ImPmwHsEn4emuj0+9gRCR0qZ9JWVuq6vJq9ehspx1thV3VI8QIKbSRVpX5dNuzQO51zQ/qjt3Xjpz/bXzjMO9QoTzsh1nORWPfOMogwKTOEcQwTuTxa9DwDshdg3N3FFNxnhXo1oASC9UFYgEU4hVUBVrjive5OjdaZDWHlqxdKUTuq5lMZ/xwQcfcHx8xAfvv8uiaUCExXRGRqnEl1LwTPClNydQVzWTyhMQKucIOMJaD9ZUiHGqRuEWEVzhP6RSC9Kzv8uVR3xR2dJMViNTIT13twC7Z7I5D3MF7nWvrb/3/QoxPvUG4uywZ9wRvCd4bwZCFefMYxgFUzCKiyW9xJkJqspgXAZLfwaH6MejxPOr8zx9UR+G9Xie93HRMda3Owty3p/dWW5uhS5a9+0QKipnbQvJiW65pGsW1CPrUyGa6AVelM5KuAdZOTeUkQ8yc+tYhOqQPSFbodytm7f4rd/6l/zBH/wBOWecg9l8jneGKYzrMZ4Wl2F7d4u9nR2W0znTwyO8y4y8oxL7CUjxMi0302ci+lmwNsOF19B/8fWKC1l5F9ZN3tKphfmBSfVlKCHREHyUe+xhxrncED7e8OEy48kzEGrCJMFXpnvoPJqNGSmnwCVzMftV0QUP4i/QKPz0jct4BGcxjAfa/1DJ0q98CmRibK0ln/eM64ramychYiBvjC0ueoiQnAcJOE1kF8nOGyGJUh0pRSCwhHRZsAvoZZUoFOHO7X3+ya/8Cr/92/+amCJ1XeMrb8pV5XqJZHyEq5u7/PAXvsBTu1d49423uHY8w7mIV0t5DpyO3CduzVD4suqfqrko2/an1T/sSu9tAjgi1l7BU1okiJrwkGREyz57Vu7ZnT/CWPcUP+nQoh9PloEoK49zSvBKEMVrIuVkaKvYCujUEbwjFiwCsbhT9XIP3oNY7FPcjEfwEi77ucd5s2h5kIcVUIW2jbRNi443ipcmaMo0y4aubvBVgODIAuocziWiz8VjsOyAtUYVnBSQDTVBGhGQTPAVrmQ6uq7lj//oD/mjf/MHBO+YjDe5cuUKrnLkmLj+wYeE4Hj+2ac4PjqmDoGtEDi88RGHt2/iffFyXMYXjyVr6ZymOhgIYO172rcXzn+GS2LD9lek8LJqSaebDUiIeSHFjqwbnoe9Op8Wr2F9fOoNRI8haJ+FkIx4IXiPU5OV26zthouaaboWBUKo0KJM68Sh2W6aT9Iar2cKeqrxRbHk/f599r2zWZDLfmb4N4YR4AqdWB2CI0VlsWhgF5x3eG+ovkfIMSJNR6hq6sq8kJxNdwNNaOxIWK9UvCDicS5QleY1EhxdznTLlr2dXVKM3Dk84M03XmcxO2GyMeFLX/wRdnd3+ea3XmMyHrO9tUHlYHNzRNANQlKmt25w8+ZNmuWMMK4KRmVeQFLr2J5Q08nUFeazUulaQ6gLGOmcO2UssuYCxILH5iJD0cq0TEjGFf3Mnm+zareQ+2OcuiT3f+gvItR9v7yIR0pzisj/UUS+KSKvich/KSJjEbkqIv9fEfle+X1lbfu/KyJviMjrIvI/fYgDAqApWfWhZoII49rSWl4AzcTSjk1QnKMg7LZeXERQOcszOO/fj2LZL0pZnfeQX3Scy5zH2W3u9T2cOKx+ck3bQY2u3nURFU8I1sVbVMkxEdvOWJVtR2oTsevQlNCcSKmjiy0ptyXt2ZJzS4wtMZlIrCvhRdM0LJuGjFLXFVkj4/GIUV3RNguuf/gh+7dvs7OzDR5Opke8+srL7O3ucPvWDeazEzNuAupsNe+y0sRMp5DFo+oofoDNsdxvde+Znr3Xkc1PKKC4KWwqvWyOwxnAqa7gE+ufU+6V+vw4U9gXHedh7uOH9iBE5CXgbwFfVNWFiPxj4K8BXwT+har+fRH5O8DfAf62iHyxvP8l4EXg10Xkh1T1vjyU1UNlv3POJvoxtmYtm5MNVJU2dghSmHx5NRlOLH8mPLQVPjuhj7qf87IcD7KPR11NzMteuciDCLDAolkybxaoCPVoRKgspIjJ5Nx804B3qPfW88J7vBPUlZJpwdSmzE9BNIP3LJeJ6cHMCum2M04cW1ubPPf8s2xuTqjrirfffou2bVHVQWLfecfm1iZhVHE0P2G6mENwuOBpU8T39wXGpxCsP2hfyu4wz+LUd7/cDNl8U7wT1rAKKRoRWtYthXQW4ZD1fX2yIcO9QM+z291rPGqIEYCJiHTABnAN+LvAz5X3/yHwG8DfBn4R+Eeq2gBvi8gbwM8Av/MwB14vjxXnrAVfLqnN3nL3gjCP0TN7rBjAPcDFe7HqHvdQ7SFKu9mbtmHZtmSBajQiBMscJVW6GGm7DukCkhNksc5YucOHgK+slZ/TQrPOkEkonhu3bvNHX/8azgW+9CNf4pVXPstiueD111+nqqpBw2O5XLKxsYFzjuVyye7uDi54vv2973J0cGi0Zyh8BpPIB9OUFGdeUWnDY+pVsgrtLsIJBvpT//CvZ6HKB1yZoz7jMWwH9Ovc9xNQPG/cL/y838L00AZCVT8Ukf8z8B6wAH5NVX9NRJ5T1etlm+si8mz5yEvA767t4oPy2uWOt/aXquEKOWfr8lwo1QMRJ/eNU/rqvQe34BfFfw/jEq7zFc5z+y867nn7OO/9i9KdFx3jzAFXqb6efuw98+WCebsgyy6jyYQggsSMZsN5aByu8oTs6FKEVEFVQVIkJ9SH0j+jGGlR3nvvHb7+2teZnsw5ODji31Hh8PCAN954k+l0Rl13VFVFVVU0bYNEYW9nh+effY7ZyTGHx8d0sSN4B9lIWllK9goZlM69eCrnBxDSykRK9++CZdnXLvNiaQ/W75H169TzaURNa9N5IeVS/MfKqDix+g1RC33ObQhaDnhZcPte1/1eo78nRISqqgghFFC4G7Cwy4QajxJiXMG8gs8Bh8D/W0T+1/f6yDmvnfutReSXgF8CVl2d+w/0CLRzVntRSFSxlCT3hKp+Pi0Xf9GR7j/Om8AHuVjnrSjrF+9Rx/q+HihM6f84taSWf3jHrFlwND2mzc/g64pJVeMyhW6diSlaerlwBCRWSFVDlZGUIHjUV0jOSO05Oj7hG19/jfff/xCRgIrnjbfe4ereLs+/8CLf/MbXAKN7v/rqqyybBnJmMhoxOzrm+ocf0i6X1oy5ZEykF8otj7qX8gBrJnft8D1zMVCnajB7u9i7B2WblbRcMR8iBey2fm79/xHzUKKApmwZDmvvVvbF4NmY7c2gq8L6i67lpa7dJY1Gf09UVcXm5uYp47AOnN9rPEqI8T8B3lbVW+Vk/hvgzwE3ROSF4j28ANws238AvLL2+ZexkOSuoar/APgHAHVd3zULvRYiYrTpdUabKtZcVqx1nO3w4b7gOqJczmv49/fDjVwPSR4nuNU/KArWtwJYtC0HR4cslktc8ITxiKqAvH3XsqZbklTxOZGbilzV6HijGIiAr6wJr7oRB7cP+eC9D9je3OXHfuKn+NIXf4w//MOvMj2aolmHEONLX/oSP/8LP8+tW7fYnGzw/rvv8e2v/RGpaanFE5NVk7rCO/BAcIovhAQtHtHgFAFZHAR393dmtZAMhuKUwZDV32dGnwrtDUq/ELmSb8/kIYsmcl909GMZzjlC6YE6GlltjfeelNKg4fFxYhDvAX9WRDawEOMXgH8DzIC/Dvz98vtXyvb/BPgvROQ/xUDKLwC/f+mjDR6BTbipSPVaBKYalXIiaSalDGoNfFepxULOkdP7u2hchPaeB/w8yLgsqel+Lmgfrjz86B8CTq+mqkVJKnMynXEym+JCRQg1k3pE5WuWiyV5PifHzLJZ4mOHjipS1eEUKoGcQimicrRJ6ZqOp68+zZe+8lP8B//h/4I2wnvvX+dLP/rDTGcH3Ly5wzPPPstf+ff/Mi88/zxvv/02XdOQupblbAYpUvlA7Ucmsa+9B6+MvFK7kmIsc5e0f7CFiNAVDEFVTWquDzvFAlELC3QNT1y3HP0DvuYBFJDSYUbVqx0glSmVQZWoAJlqjZIHVPi8K3JJr/JeAPe6R+mcG8KLPgSvquqUYegL0i4aj4JB/J6I/NfAHwER+GNs1d8C/rGI/A3MiPzVsv03S6bjW2X7v3mZDMZdEyYWA3rnrcbfWZeMTg0KU8C7gBOPGsUNxKE5GcotsC7ffPZy3OvBvChc6D93vwu8vu39boT7gUrrxuvhDYUCpT9IeTL6ldGJp2kjdw4P6XCMd3apfIWEFpVAjrCcL1nEBcumJeXEKCe8ZDwdoR7jfE0q3IpuvuSnfuzH+Lmf/wWu7G7TIfzZf/fP8NJLL/Lt1/+Iz3/+s/yVv/KX+ewrL3P75i0+eudNbl7/CJcyPndUldWESFZUsmUpxFF5xyh4KgEl4701Ae5yGh7xlB1NG2k6a6wTBVqFWHAHaxzE6mclJ2O/xQhYxU+wu6xkawIWOfjCuExYQ2InGY+S81ptoZrXy5l77F6YFFx8v1z0eu85iMjQtNgqba1gMbi+65mwu7vLtevXL7xDHimLoap/D/h7Z15uMG/ivO1/GfjlBz2OFFfvNK6wYs2lGIldZ1WawkqfMukpsPKUG/IAi/9lgMOLwo4HAgzPfO4iXOG8G+o8w3Z/T8WeCLtn7danrM7qHF2GW4dHHMznPB8COVRUxfjmpFTLBbPZCQmhiR05N6AtThIbzsDDZgHZZeJiyVN7e4yqQI4d1XjMN7/5DX71v/sV2ukhf/7P/gyf/8wrtPMF1958k+WdW6STA7x4tkMglApT5yxzUpdaHO8gaCY4sfZ/Yt7VxJcKYEBcQKoxMcOsbZjFyLKLdKpElJiVhJK0Bx61MCV7opCUYjMrLjt1jUpIIUClgssW9kQFly3lHlVJPbN0zQO513U979/nbb9uJPpy+n7h7EOLlBLL5ZKUkrUtYKUturOzw2QyufAYn3om5alRDLDzvqDd1rm7iZEYo1n0EMgJ0yEooUdvJID7GoZ+0i8iNJ23/Xl/32/bhxlnw5uzAOWDG6Mzqt5DuG2GIwPzpuXg4BBUGI3GBBwpVKSYqRdz5LgmtQti07DsGkQdk7GQXaBNmbZZsLE9YbQxYby5gRMY1xWH0xO+9fWvc/2D9/n3/9Jf4Md/9ItMDw9445vf4Y9/7/eZHh6xWU9wWRkFT+0DwTlrq4hV8goFgxCr9xDtV3rPSv3cbhrvHUkUCdYFvK0qmpRoNdEmYRk7mpSI2eBJ57xhXINXcWpiba5POwPD4frXMgyeiNPV3xcpmV2UoboXUL7+3norARFha2uL0WjE0dERy+WyVMF6ev0LEeGDDz6g67rzT4gnzEBoAYnNZXPWxj1GmrajTREXLLUGUnQoO+LQ74C74onLPq6fprz24xty5m8t0diqoNkBXcp8dPMGbeoYb4xNj8E7YtMwGk2o6xHTo8hyOcelxCiOSb5iERMfXL/Jjf1Ddvau4kcjPruzWUBGY2V+4XOf42d+/Mf46R/7Ijc+/JCvf+1rfO8b32J65xCHsrFZIzETFGp1VPgiMGsuvZSQxokgWQsuRU/PGr6dqkJhfFYp4rxpXQSEiXha5/Hi8TQ0mogICUuLxvJgRxxaQoe7XdCVxH8/myKlBYNiFasuF/7G+V7m4wCd+4XNOcdkMuHll19mY2OD9957j+PpCalLkO39vg/p4eEhi8Xiwn0+EQZiiLXpUzNK1yUW2tC1HTFlEI8i5iZioiNRTS7MsKE+3Xm5h/3sivyw+eizn3lc2Yez3sR5GZaH5Ww4Eau/ECXGxM1b+xwdz3jpedPfCFIxniSq0dQ6cKG0MUIb6ZISEU7mC77zzrtcv3mbK1cPePHlV3gFZdksceJo50teef4lPveZl3nnzbf5/d/+V7z77ttIGxkH8xaCKt45qqzUqkjRk/QCle+FXHq2pMOpGA/DCc6Fcq0F1UhMHV4VL3ZvdGrl2wmonMePRtTemVeRMsuUiChoJikkySW8MAPh5PR9YPCCnUssAKrDFVgz2wP5MWUzzt5f6/dAz0i9iCeTStnCReMJMxAlxHCOmBPaGlkGMZcwizV96YtqsuXDSv55/QFi7e/z3bp+XMZtv8gFvGzG4nGMi4zCRYbuvkNWn50tGu4cHtF0kVE9pg6B8UZmsrlFGNW44FERYjZh4Khw3LTcns44bFrccsEXdne48tTVIctw+6MbvP3d77F/7UO+982vcXTnJsSO2lkI4DWbuKxaOEF5WMEk53oRWeecgdCFCKVg4OAAGitmBiIi1kjY0o6GuaScSaqIr/BVxSgE5l0HAh12/E6VlBUKbXyYw34q+4SIlPstGZs0i0KWUuYup+69i67Hg3Ahzj70fRpTRGiahmvXjEVwfHwMGKfIvMPTwPq97tAnwkD0Q9CSLjL03fsKL57YRXs3202SpIBOPL7w4DIYxHnvrWMaD0RkekyexgMNLVWMzuGykpw9GNPFjNsHd2i6DrxDvKeejNjY2mK0sQG+qDehZiSycrJoOGw6Ogd3jg/53T/4ffauXOVLX/4yVQhc++Aa337t23gSi5PbBJ/xziGpaE2uQYTZlbnLYNJ4mb5psKAkzcPflIXB9UlPLQu38/RgdR8IOGSV1UqCd0JwkFxHdDJUuIrkkja1LMaQwexX6eF/lFUsIWr+gxdLn3prFlC+09rGBct42LF+n1RVxXg8BixrcXBwQIzWxrD2AXVanpXVZ+u6XtPbvHs8UQbCLLWasKgP+OBJXbZWesncvJjsBv8kUYPzHv7zyFXfl4f+zLjIUPUec1a1uhZv925Okelszv7+Pk3bWphWgOJQ14S6RnyFwYXgQiAq3Dg4ZNp1+HHFYj5jsX+Lk/mMzckGx4eHfPubrzGfHVMHh688bdtYhgO569pp/7CL4SQ5Z6Nz98nZPnXYV2FqPvXs+T4Jof2+iryeWmm6XaKEU8ELjJyQKlfUr42+3ZVQo09t9nL4eubp7tdjKaxMdQyZDVFdmSg9N5HxwKNnQ4oIGxsbPP300ywWCw4ODgZVd1XF16Nzwxvv/T1P5IkwEKdibOeo6hrnHYu2oVs2dF035LAzdvPC3VqNq3l4fA/qeqx3P+7CJzEuE05c6FX1mRGM8OPEwjVS4ujomMViToyJkTcNhlAFqnpEVkernqgVjVbcOVly6/YRVVWzd3WbO7ElVI7Pv/IZ5sczfutf/ive/t532JpUxNTSdB0SVlmDjA49P1UtjBTvhkxAUgVNBlQXzMEWeIMmnThrhLP2fVN/DxV3PyNW4ampGBlH0oTLMELx3tEpdIh12coJSRZCZWEwY7rmrpudWgHiIqaV6jwEFbypKVNm1eqJeiLWQ4yzYUYIgdFoZM+DqmX2ynZZixo4pxet3sO4aHzqDcRpAM7IMYIQY2KxWNC2rfHgtc95O+ND0HuA/eSvkGd7bh6/kbB9Xxzzfxx4xDrWcTbt+cBDpPS5MIm+nKK1vYuR/f19Dg+OiC+0MBrhgqce14w2NojqWHZKm+DZzS1mTUszm9POZpzkDtdlPvPSy4xdxa//03/O7/z2bxPbJZHMsluikvGOntNGVlMmN32FRM7JunAX3ktfg+PL95RcmJBaxAbFFSKVzUfWtEp3l0zNKhgxTwTJqCREleAEjxmZII4oQvIMbF1TyCr6GWIOS8/JG6bSrg5uEOQpPAmhNOgpG90j5bl+Hc9Lu/fXe6BNZ+X48IimbYsnpaWWSUgxnuvV9mUKF41PvYEATt34IHQxkVNc5W9l1bdAnEOdXwOozvvyq6tyEVD5oOe3/vv7MdbPYd2buTQGUt5KOXH16lW+8pWv8Oabb3Lt2jU+9wOfpTmZMZ/N8d4RghXKOW9aG4tmyXR2zHhU8+Uv/yg5Z17/zmtI19C2DU/t7rA32eRf/f9+gzs39y3VKbBcLshYpy1XVnxHyVzgKE23SkjpVw+ElI5ZYixQ7wTLPpo4TBA3qFeD4SiqeRCVddJX+OZV+AIMxdxKSZSaGEwP63ksCwKU7FjhVaphY5R0p+hqPo1SYrqaZuQYmgifDU8e9Hr317MnPTnn6LqOrm2L9ipnAJK792Eg7xMeYqx3hR5cpyKserbwRFyRBjv3of9kHuDHYXS+n2MymfD888+zv7/P8fExr372Vd793pvcvHmzuK8MK9R8PmW+OGS5PGZ2nHjtj/4QgK264pWnnubl555nenRMc3jCIh4j3pNSZr6c0+WOuvKEbA9ez02onTNiFCaYy9nVtM8owJBt8WKwZN+M2GkRoFUGAlXRsKHvZO6cQHYD7iHaa1YDKE7t4TYsoleRWulPqK4bF1b1IT3MMCQ7xBCa4nFk5+iLKB/FSKwP50zpPaVE0zR3pTYvGj0l+8L3H8vZfcyj/6I5F51A6Svn3FAO3rtalHAjDwzKT+bc+nFe+umT9iwuk4q9KH0rIty8eZNf/dVfpes6nHN881vfpD2Z89FHHzGdTrmyu4OzKgSms0NOpocsmyk+w3vvfI9JVfPZF55hZ3uH568+w3e+8W2OTmZIVXO8mDObT0naEbxRpiVGKjHDUGGNdF0RxqYAiSU/imZ7cA13KO5xyWho3yyppEMHg9I3DEYGg+LIZBzeKc55+gUXLQVYBQLtTYJgXo4r++kNVjnFIW8iw9ye9lK9mMfhxbQse7ys52r0m1+Udr/oYT8VXmKNjzVHBtk7NSm13sN+0GXriTIQYHFjxhH6ONR7axlXyB4Gaq0pCH3CD+j6ceHu8ONhUp73G/ej4vbe1WVWlH4ej4+PLT1W1xzcaanFcXx8zGw2I6WMl0xwnvlyShvnCB0vvfA8n33hRUYuIMD85JBvvf8+aZkIIsybxvRCC0+xFk9QU1gYu8DYeWrnjP/QK+fjUE3Dg+Sk19AUE40hl7TjGkVeBHWCZNOHkALUS8+nKFWbgoWkrq/bKIbGaTE2vWy/WNWj9ejwZC3dzPuUjhc0C1EM8NSSVemNiJOiAl7ii5xNn7/LiuRc2gDAullZxyDOpsv7cdcCWJilwVlDZpyF2ZqTEcpCdQpucM49+SBlP/pUjg8VdVXhXF9kpEN7PbG74LG59/d7iD8NYcQDZyoecH85JbqCws0XC46ODum6lsp764D1wbscHd9id2+Dz3/uMzy3vUecL2gXDbfv7NNNF4R6EwRa7Wg1ksT6VASESoWxr6ldKF2xQHK2eosSLhQooYTKbgiZDbOwa561gIHSGxYZQolTxoPicIisLaeC9wJiYKfpT0oJOYqEXcEb+lCjct6aBQGUdoDWM1QKxrC2/+I9SDFa4oqHxOp4+cwp9S0K+2t5Fltav849tboKni521D7gy85STsS8yrBo1qH8+zKSAU+EgegnxntTWa7rCoCc4zkW8PE9tN+P8OBhxuM4z/5Gq+t6AARzNpxHu5bYdSyXS27dus1y2RCc54033+D117/NYjHlB195ib3dDUJO1MExPTkizqZMQk3EBIU7jTQah5RlcI6xD2yGEbXziGRcVlSlKEivDADloae49a507wLLTBSUqvwnQ9LKHrw1UaHemz+VaVrzSoRB48FKKYq2ZSrnJA5HOidjYeFD/5T3gGgfQWTMk1ChgJU6fHbQqZKV8bPz6hc7GV4/j1Y9Ho8ZjUYEgWo8JoWKUAW89yzmC6azqckEAi++9BIvv/IZlssFb7zxBk3T3PO+eCIMxMo4BLx3iDPLG1MqMZfixIp1DKhUS+Tz6JmFx8nEvIgO/aj7fZRteuPSr1i9JqRzkFLh6otHxbp6L5sFTTNHUsPr3/0WzXzBqy99lmefesaAQU3k2HF4cIdQ2K5NsvqHNrZk7ai9o0pK5YSx90x80aQw9JNYAklKrF+Wv9XDbSdO70NYNjuvXHSzAAUbMMOwcuLN1Rc1rENFIJu8PfSSMObdDJiCOLxPuJRwyYxSIuM0lIjFHn5Lra56flr9RQmN1FSvFCNNKWq9P8t52bG86ZhoKTxzbnVGTki5K93T19LZpR7s6aee4gd/4HOkxZx3336Lk/kJde0QNyYToclsbz/FX/yFv8S/+xd+Hsj8k//Pr/A//Nqv3RMmfSIMRC+AUVXVYEktr71yk6qqNuJHSjhNpGR58U+LA/C4w5F7gVbrrullgdreQKSUCMFjTW9A1UMQJGVy7uiWJ8TmiLaD4/33efHqHj/4yqtc3d7Ga2S5bDn46CbzkyXBj0gSmKfWGvHmxESgypmtENiuA5tBcLEh5mywmipd6iAIdQgGrqVcMAFLrTp6L0IGUDKzwguG+B8ZXHhx6/mJAuhB6fpt4KYv2RCnoHgcGQ/UXvHOGhVnzSyj1f+IM51Jp4IUbYquU1TjoKytGXBFu7IwNTOFq2EUhaEDeRUC44mpee/sbLO5uYUr+hs4oW2XtN2Srk10ndWW1KOa8XjMD33hR/jZf+fPMHbw2//qN/jDr/0BKS7JybQyRq7mhede4Itf/lO88spn8JXjP/zFX+SdN7/Hd99688L74okwEP3QkrpKKZOKAGcPVFZVNWyXVUj53uDL4xqX8QY+SaziYUhS69unlJjPC9/B1/hQr/WpzKS2oxLhzq3rSI78wKufY2sywUtGotAuOo6PZ/gwootC20TmKdPlhOSE80rtHJPKWe9PF2hjg2o2DQ8UvOKliOFqDyiWMnRdhQT+lEsh0HMXDAu0So4y9aW2q2QqitvOSoRoNRcllMHidicZFcfIV4wqqLUl5FjarOQBo6Dv4lYw0B5M7SuJtQdInBkN58H7wKJtUKCuK64++ywvvfIZnnrqKZ555ln2dq/gfbDKVAdd4ZUsl/aTUqIaVcQusrmxQdtExqPAD33hB3nuhascT4/4zd/4l9w63GdjtMud23f4w3/zRzzz/PM89/yzTOcz2tj+2wFSrmcHuq4jpzi4xr2RGNzlpINe4SfFfbjXOX+SOMZ5BuJBDFSfHk4pk72jUo+kiMSExkBOSrOM3Nk/xknN5vYOV/a2mHjHya0Dbn50g65JjKpNYmpoUwEmSThRJi6wMx6z4T0VEGNnWqI5D+3ugvdIYSpqzyfQlbzNkLKkf+Z6nCKsuor34UkJMc5MUglOVgIr/eIzbNInOKVcP+cJQalSYpQ9OWZiASKy9FhCCQsKgO5ECgbiBkAStd99oz8vIMHz0ssv8aM/9hP88I9+kaeeeoqNjQ1ErLuHcwHnhKoOQGZRDIQWqbzlYsFH167zja9/jZFkPveZF/lLP/8XSbljcbzgv3nnv2WeZhzOW371n/53vP7Wm7z8mZd45923eePtd+75iDwxBmIF2Chd7IwSW6S1+oeiz2b0Ne6qK3CnHw/z0F7EGTj73v3SjY9ynMuMex3/sp/pwUpVa5Ij4nFZCQXRb9rE4dGUW7cOcGFEl2E83uIzzz3LH71/g8P9Qyo3IrqMDxXaJFQtK1GLY+I8k1CZhqQmYoyGJfXzBFbVWYJrWbt7e8nYwZug9wrW+AfFkDjXg4Yr+7B+LQYeRfn+A6I/TJeufpUH2ysEMZZnNzAlVsM7MzvBmUaGK2nVHtMQ12dZMilDih1ehNFoxKuffZWf+Imf4OXPfJa6rkt47E4biMojDkZjUxWPMdLFyKgecXx0zOHBAZVGPvvy8+W7O1556WW2NjaYLiIuCPP5nD/+6lf56je+Sl2Hj1X2/hMb66h67FpTBgphoJf2v3sgcGjZ1hNoHvHBe5TxSVdxnqVY329bOfOA9F5ZSsY9SNGcacURu8zR8YzrN/Z594OPcF45ni65c+eYURbef+d9UpfZ3h6TopCiGenYRSRlxlXNVl3jcyo1DZmYM1ETKNRF87LACIMB8OJwQYaK06HjpsgQRuT++1A4C7LCKnTte66EaRmyHwqnm/dqaa6D3UspJ8hQuYqxh5k2BLWsQ3Il39KHFgjBWfm7mQs72DrBygkkB6qOnBKx6Tg5OmZcjxiNRsM9HUJdjIQfOoQBjMfGmGzb1iqaU2JjMmFjc5Nndrd49XOfo2070MiV3V2ee+ZZFh/csJ6lKD54Nrc2GI0qFlP9t6fcuzcATqwWoCqGob8I69tJb645f5V/EPLQap/y0A/gg47zCFaPc5y33/7vF154ga6L3N6/Q9slvKvAeZYxc+fkhHc/us7hYkEmUk1GxHnDd776TWY37rBdjYZMQhbr/q0pUSOMcFQIQaFNiehykXJRvPO4UAx9ykjusQcDGHuhVVcyFH0oQUkbSskQuOLuaxEw7g3NOpMwaw9T9h7m+s1DMThrJOqsqCZ8VTEWz0aoySRSynSaEd8rZTIYBesTWtKufYjUZ2NYeUq1WOHhW2+8wTde+zrPv/ISPtQrkBULt0yI3dKgUq5TXde45Gib1kDP4BlvTNje3GI+nTE9PsIl4ZlnnuXajTvk7NgcTXj2+efY2dniZHpMMz085aWdHU+EgVivWLQ0prHaXCnKsYu+7irZhZdSmfiwtRHrn1nvHXCeW35e2HHKpX3Ic3iQ8SAG76IxmUz4yle+wu3bt7lzeIQkxdWB2LS8+upn+Mmf/kk+fP899k+mdF1DkJqTmGgO7rAXaoLzaDa5HmtE0DL2ng3vmVSBWhyqmSZ2LF0p2c6JcXD2IIgMD1D/4K4Yieam9y5/H1z090MvMbNqsOuKWuz6nKzIU7ZvzrEPunZ0219MCZFMqComo5ookSa3JUPicXgo4shD6DMApOZe9LUaroCjkhXBsgzLxYx/9Vv/mudefoWvfOWnqMr9vTpfO2c5EzJp1gGDc85z/fpHfPvb3yE3M473bzGbLphPTRrQJ8dkvMHmaIJ2icXxCV3T3XMRe2IMxProXcnztrMbg9WNds6D+SAP0vrkXeQ9rP/7SSBWwcUeSYyR3/u932M6nRJjwgdPmxOvfvYz/Pmf+/doFzO++9ZbHB8eEdsGbSI76th29qBoWT1T6SylWRk7YaMK1H4FCqaUiJoI3lH7inFZNWNnoraurOS91mN/zprsDQ8Fg9ZTIcGATfffMZ9fDqXa90g5bbwH4yE6hDRa5sX5RKCiDp6QkgnRqHEoRGVlWEp40TeN1MFLKedSDJzhMJagrUNg/+ZH/Oav/zqvPP8cn/vcD6CaDKhU08jqsROHDgtfr3lixVkwPZnx3rvvMz8+4PjgNm0bOZ7Nh/YQi9nMgOSuoWnnaNfeDeKujSfCQABDo97eNUMo4FahmjibrDZ2QxGMpc7yPY3Begze/7sHPs96Luvbn/38updwHi32QVf2B2VHrm9/Gfr1RV5Q1yVu3byNc54qVIjzJJTPf+ELZO/5Z7/+65wcH5hmpFeadoaGEXU9GdKQOSkSKlLu0KiMaqgrjw+BrBC7FieeWqGWwFY9pi7HaVIykNI7fOEmDN6DGnXYCVY3IStNp96/y1kHspP0eIP2MnVrXgO2z/59VRnYjYqWFoT27z7LYqC3VWZWotQu09EZxhJGOFeRSwtOYU04tg+LSszkEXI24wLOwixVJiJce/O7fPV3/jWfe+lFXKgxY2WgbcrJ5ACd4LwjEIZ+m03TcHR0xHQ6JaA08znHUyv77rKj00zTRVxsWDbWu0S0g26B6MVA5RNjIKC/iUFLMxJxzlqciU24ai5dku4VVZ2/3/u9d17Mfq8H+GE8iQcxKA9jfC6VZSmqLc55SwOqYzyuef+Da1y/9hGH0xlehOA8lVpfzqoSquDwaqovPQiYUiaIrprcVBVt09BlkwWsxFE5T+Wcud5qZd8CiLcSbc09pcmV5b2IthV/fZCM09JOLmejQ69/V1mFDetGwuJ5zDhg/TQp3qc4M0jSN16yg5qILhBI1GQ6cuFYlMIoEqJpBYbACh/RXNgXPSZi23QlFeudJ7cdX/393+fHvvwlfuTLP0aXIuKqgXDVi+5LAVhSn83oOqbzKTdu32Qx30SycnR8QtM0zOcz2pTIIii2YFYkXO6oPf/2gJSnhoqZagbbXGrs3V1x5bkfv+ChepBU5aN4CE/CkPJgORe4eet2qZ8IuKSMgiMkzAtwgToEgjokmhGPWYmxM26AGKOR4gX2HZ68c9TBMKQ2RXvonSNpxquzVV8ZRFouGj1jNOeMqJKd3Pcz5+3jlL7zWsqz13PodSECJiEXEKrhgTd17eLYnFGYMuHaFXHLerpYyXcyujd9iKQc3r7N61//Oj/6Qz/CaLxFdKaJuSKsrZ0jq5e6FJm1S+q6RlQ5XsyYzeeWiVGKm2UemJdMJcrO5oTR7OJ6jCfLQPSL3JB/tiBVMRfSwgnuicpedtwPp3hUQtL3e5yHy6xmToYfJ57YJkTs4SALHj88LLVzjLyn8t66f2erqtWUiV0ytWqpDOgr7nDOmaqyqtyqAMltMnUwk2Z3pUO7wz3AtZR1BO/BZqN8dymZD0vv9pkIV9Kp9p3t3gsIAUfAFf+m1HX06/vKvvS+S++cMChNlHTscMaFZRmC5+3vfpfDW7d49rNbxvvxFVZ4oavnIGeTweu5P4CvK6g8bduSg6PaGEPscMtE0kjwgkepRNgINT/5E3+Km8tvXDgzF7f1/RSNPoNgqK4jqZBUiSmSNKJiSalMRkXJyUKOBxGMWccQesJVj1/cq+6h3/77aRweF9eiL4qzn2A/LhR9xiIHJxAEfE54MhtVZaIvmtAciXSoJBObAUbeukv7KrBsGmLXmedQVQTvyWqErJQt55HLSt538A7OD/G8croMev27nwr9hv+ttjnbwbo3VIN36O5OYfcNb9fDS1fCV+/68MgTDBCzUvUUreOXKqHgFUGhwoq5vIh5IE6o64oqVKZtgkLqCAKT4JkfHfHeW28SVAmaqYKAZiRzqlTbe0/TNBweHhKcZ3dnlzYn7kyP6XJivLnBeHOTajwmVB7vFO+UqnK88Pzz/PSf/tOMJ+ML74lPvYHob4BTD4EaNJXFkRBUPOCh9DG41D4e8BzuNz7pLMZZLORBv9/ZbYfPlypC09swMRVrU2kCrJVAkIwnUUlmHBwjF2xl7RH1nEipBYEQDF/QmNCY8AijUA1pvKzWmT17GcKLlPNwHqcTjvfGgs4DduWMoTj7t3EL7p6zu+amxP/Wq1MGnKK/4zQmiBlJSihhSC1C7YSq/HhhkNbzAlVwJpAr9iAGVcYiVFmhaXj/zTdYzk5woiu9irVT7edgOp3SdS1f+cpP8ud/9me5evUqs/mcZdvSRmsCNJlM2NnZYWNjg6oKjKuKne0ttjY2n3weRD/6ngKrTHNx17T/29FLit9r3A9cvEwG4SLew5OS5rzcWHOSxUA4RyKQ8JrwJGovjLzvqUFla1OS9l4YjWuqkmGqfBg0EDVluhiJORVg2ejROuCEa7UUZfQzu5pvA6aHh50z4JOuPqhrnz/7HdedP5Geh9ErfKu5/q4wO8q5irdMS1BPVGv56PuFK9t8eW+1JX1JtxQlKzsO5GyAppTflTgrf6/Nu7p9/Tqz42O2N7foclyFKgW/6NmUJ9Mpznm++KNfRCp48523WS6XpgfRNDgRxlXNeHOT3LS0ORO8kNrIW2/atheNJ8pAMKSlVhd7qNLrkeqHjkMvewrnrUJyykg8maM3uv3kWus4pTPKsprGYRDrph3IeIRKHF6Sub8C6iltB4TKV1R1jcaWrjWMoaqqIWXddR0Jk1zLajF0KBkN1WzaigMFeqUWbQt4b0BOe5Z9tef6t1LEPCLtN1vBkYMR0gxSSs5ZpVZVHOoc4jNa+r2KB9Nu6Fa4g/bl5r0xLe55Sc/2uINRQXvcTIuad8mOeMeo8oy9kcaO7hxyuL/P3ssv0eaE85UthoVfYo5Gw/TkhPlywZ3b+7RxyY3r1wGIOVElM6BRG5quwyugmRSFo4MpX/3jb3J0dHLhXfFEGIic84qbcGaFUAxQGtpvrt0E/XjQVf088PE8A+CdG9JNp9h67uP3Ii4CSB8kHXs347MXAjbdBRXAUTQNk8XT3gq3KpzhD84bPuG8Sa5lTDXUe6pQG4AWbfWr6grnTVWJXnqtXDgnjsDKZXfr4F0vBVfif7sX+tXel+tTUp8FpB6+c8bEhPKqglJRgu9xLYYsQlYpjXlsMgYNS0x3oW0iTTujllAIXIpIRkh4J2hOVMGTcuFLSBFUBoSV2np/DE0JzYmREyZhROWUsct4NRr3chnZ37/BK9rhqppEuc/KT+oizbJhuVxy584dfvf3fpdbtz7i3bfeLrUiQk6JXLQnu6RUzpfnBm6fzDiYzZgvHsGDEJH/O/AfADdV9cvltavAfwW8CrwD/EeqelDe+7vA38BaB/wtVf3vy+s/BfznwAT4p8B/og+w3Bo77WK3XlSMuppPmZBHG/fY0SBg4lZNe2BYg4dl7DJf8VHwi0cNac5nmWZ8cPgQiDmSSQQyQS095rGHrfaVPdTZcvgxKaXuyvpmOEfbteQULTsRPIlebQnEe0KfKXDlM4i1/sPm2BrU2Irc+ziuTweUf/eNknJhKNq8FHMgxsXoJez6QNWvI5krW0JSW99zmYus2TI3VcC5jhhbNKiFF05tPlyRq8mF+p9LenbwdO3+VBESWipB1bpsxUhdV1bh6lqCSwWMN0Dx4OAWbTcneiNvhTwarlNfPr5YLNjf36dr5hwe3KaZL4htiwSPukR0Qs4mYpOKkJ9Dhk5b8R5g/mVAyv8c+J+dee3vAP9CVb8A/Ivyb0Tki8BfA75UPvN/EZGecfp/BX4J+EL5ObvPS4+zK/qDEIUeBqi0m7Jf3foqwZWI2WAQ1n/Wjvkw4zzOxXk/jz7E+CQqg+ufc6ZrWzRny2CIK7eVUZyFIoxi7BMYfluFJmX1dIgtEykP+8gxkmNC04q5iiqa1kv1c3H5tdCNS5ah/GB43SDO0hOm6FvbZXuwh1BCrJlOJVaKHcSaE7uc8SnjkvW+OLUilMWGZPs0AlZh8Q5itr6oUNkilUtfee8EJ31QoRaeqQ68iYAQRIYMSKXCSOy8LNPhCGLbtE3DyAc75+Eq2Jm6EHBeipCMYQ7j0diMVMrEriN2HamLdDEToxKz6WRFPK06lol7onb39SBU9V+KyKtnXv5F4OfK3/8Q+A3gb5fX/5GqNsDbIvIG8DMi8g6wo6q/U26K/yfwPwf+2f2Of2r0oWb588L04z2W/gdZ0ftDypmV5izmcDqe5VQ+/UEwiQelVj/M5+7+fIm57VVWvH5bneqqxqnikpoEmxgYZw9nX4DUV1dqcc1NZdq0F+1YfTjWxW4QGnbOFak2bwAeRTBW88oIqXWk8iIWr0sf7xf8SdTEXn0PnYipYTk/0LODU1xMpaMWQzFX72oPBIX1acy9YbAPOFWC63Un+w484CnU5+wMhwFjROZUzrHHTrJJx6kQpBC6MMMyEkdQBqPZNwVSMrdv3GI5nTN+epNF6q9X8dKKQdjb2+Ppp5/Gi1J54eTkhJgiXS/JWK6XsUX7HoemhxVT37z4/PGwGMRzqnq93GjXReTZ8vpLwO+ubfdBea0rf599/dwhIr+EeRuDUhQUwyCrm259FV1tYze2bfvg2MNdn9Hz3zvv4R/+3eNej2AcHtYAPChI2mN0oQqIUHQ/hcVigUtKjkpwGafJVJMra25TC9SlPybZcKC+XiFntXRl1wGRqugbrAv6iBSBHxEq7wawWdQa7ebO5ASDM8ZlKB5cb3jXfDR8HxL0AKEzgVh7WE0wtmcx9qXSw76k94FW2QzDD0wcJkketB2CW6mWqVqY5UWQXDQqCpGpN170TFDv8N7IUykVA5wiQWBcVeY1FL1Lk6vLIA4PvP7aN/kffu3X+Nm/9Jepd585hcMAbG5t8vnPfx5VZXp8yGRUc3JywtHRUcmQgLhedcvhgulM5JTpYjLtu3uMxw1Snncn6z1eP3eo6j8A/gFAXdc6kFXKzWxtDcT6cJ6J36Uk3Pp/nyXInHvSa9kIMDdS8+phP+9h7fUk1slYw3ZZT0Pplzz++n7Wv9dlHvrzjMj6587bx2BknSP4wJWre/zwD/8wt2/f5rXXXiMRyTHivNVaVE5wOdlq6kuvSux6mFycPWQZyA7arsWTmExGhqSX8KUXAIKC56dEr88kWtJ/Ym3hgvcF8+2vfwEi++87RHVawEbr1Rm8PazBOSQWhWzWbrq1z1PmuzD3EcwLQRw5x2EF7u+lrJmUFXFFuCaDqJVtkyPOB8QxVFi6glUh1mck50hOHVWoGIWq4BVKKGGZQ1DnWGaFGPm1f/rPcZvb/Llf+CuMNzaQJENHub29Pba2vsRzzz3H7RsfsZxPGdc1B/t3uH1nn/6COCurwQfPeDJiuViSNZ7/ZK6NhyVK3RCRF2ye5QXgZnn9A+CVte1eBq6V118+5/UHG8W3crKquLzfz9lxqTi+HMP3mENJtfWprx4s+36OyxqNS3kfajJ+KSX29/e5du2aZRs0U4tQKYy9ZxSCpePK6uk0lwIkVn0poUyOPbDixRiTxXvoz70PYdAiKpNjUSlPRXTWneq5amlPLT+9OK2sRGzFiqh6wVpbNc1w0Ge/em+TQs0vxX1ZDDQd8KTyPbzzVD7YgpRN57pPw0rvKQzshGLgyjGds4cSMqomu6eopY69VWO6oqxr2kZ2v/lS4+EVKoRJNWI5m/MHv/d73NnfH0La3rhPJhOefvppfvAHf5DPfuYzPPfMszz7zLNsbm6cvsRlbsZ1xWRc4XxGxHCje6HxD2sg/gnw18vffx34lbXX/5qIjETkcxgY+fslHDkRkT8rdsf+b9Y+88Bj/eHurfp5D8zDgnjrexJ693GVfuuNxKdt9A/e+s+9tu1/iwghBNq25cMPPyQ4z/bGFnUIVN4zDn7FCpQetJTht9N+n+v7B0r6EmHANdavV5/9cSWuN9kVWQs3MPe9AJK9JmXfNEfySoRlJcbSy9DpcIyMkl35jbEhTdKmeDvnoFaiFmIE5wcRmr6Ktf93kaKw7aHgL/09s1pgVCGqKXsn1VJnaJZMSns8cav+n4J5JmNfMQ6W4dj/6Cbz6fTUyuRKHUtKieVyyXvvvcdrr73G22+/zWKxOBWa5zJ7joxoIjgheAYDf9G4TJrzv8QAyadF5APg7wF/H/jHIvI3gPeAv1ou+jdF5B8D3wIi8DdVNZVd/R9YpTn/GZcFKIez18FF7AtdyjFtFSqXeF38tCfW2Hbn7HPtxX5//RpzahTwQ1RLezVd23YN6Frf5QOmLh821Xm/7e/CSfqHoXzMla7YKVu7mp2dXV5+4QW+861vo8A4ODaCEBx2cznrMlWJp3J16YA9fOmh9FlzRBVEPDlL6SkhJWVZjDuGEXgpQKTFc6TOSFc9NdlpUYWmKFH3Yi6qQ5rZvBCL4UuO0gxEzsM1NV7Hys3py9J1LTPQl2BaL04HAWIqIrTogE1kjDjlnRvUq/vFoy83l6wkKd5Ksns0qTEyvXOlp6eFqL7v+SE2JypGya6cMA6BThzerefO7Dsvl0tmsxlvvfUWv/mbv8mtj64xm81o2g4XKmJMFiqJdTFfLpek3BFTXnlW9xiXyWL8ry546xcu2P6XgV8+5/V/A3z5fsc7O8y1728OHS6ihY468P+H0lqxGLIPA05Z+H6THvAiDSuoO0NuUgVkfRUulyb3qlUlxMEwAgOYT0W5DzzWRWsu++Cfx+xcf3/NcbaVRFi7KRyZZCGUE5JGUrfkcP8WoVCAJyPHqHYE79BoIUBAqKQmUJE1kUqxnJQ9alZybAs+sEGOkNTRJVspR6GAjmo1BlJUr6uCG1iM7ai91SpIZm1aV0pPzlvqQrN1V6NkPPrQQ7OdV+V7MRhdgZJa7hCx35bJXM2Vc0b4wikhiDViSibgG4TSTjDhR4E6eHK0dVCyhReV80Rn5jhpHryiHp+q6gpHNoxHHM770jNUjaRGposLQpigmtjcmLC9tWnELi9Dsdly3jKdTnn//Q94/4P3aRdThhqaeoQ4Jat5D1kz83aBdHbfx9IQ9F537BPApJQBuTWvQC/1DGrZdngU1gHNAkKtHvbzsIozXsf5Z3YXiPlpH24IPaTnAg9ovANi23HcHJDbhklVEaCUOq86WvdXwy5F79NhE9Z7UmLGyH4s7Zmlz+KXmFus2lHE4VgxJb232D84y5Jk1ioqswGOZzVCe2Pdg7qasv0gA1PyrjG83nujPeB89/XsAWl1BQdRe82J4ENAkiJ9u8fcZ1IKVRvWVM50hZ2sH0mskY55VYEsVkq+TKZ2deXqVTY2Nst9u/refVbIGviOOTm8jfcV49GICCyWiZjMcyEnw0MkD4TMvjfIReMJMBCrh9gWAZtWe/hXF9JeXf/3Kh48dzUu+MVFNRQP89Cfjes/qXGvlOi6/zPUChW8TqA0qDGp9lGoIGUWizk+Z+oQ8KKDqnP/AMrgrpVUn/MgWsIItfL7NdCvF08pUQEpZ1zp4B1CGNzmnpsw6C/Qe4P24ZK8Pp11EopRKJG/9OrXRXOh7COvnqrT8zbsaxXTn3/teqNQ0pGymmtXPFBNK+yhV7l2UhrzYt5nztnmS40bos7wl6FJEPbg2iV1dF3Cief5555ne2vrFP7WlyBMJhOeffZZXnnlM0yPD9nd2WO8MeGjmze5ceuA6WyBpjyAwaxdm/MVO1fjU28gbGJtJejBDOPPF7ZdKSLqL4IlOUO5eWTIe9+1Xx2uwoXGoQf6VlyLPgQ4p0RYVmHPw46z6dbLbHtR+vKcT5QIqPADStGP3c5CPa7ZHG/SHR/ju8zYBUbicRKpSlwcY7LPFCvTE5oqLERRb1hGbw36r5HF3Pd+9dJcWtE5wTm19zAykrV+sGtn1Ok0hAvlneG7Z13zOsQBPY+gpAHVwD7nVp8vJma1fBdnKhf2ZT9//cN31uhrCXdELBNhU6E2n+U+7PtxWPRS0uGa6TSRKBwN1YKtmJck3llLSbS0Bs4sUmaR1F6PSkwlVFm7T7z31HXNs88+y5e+/KeYjEc8//zzbG9v8o3XvkHzx1+jWy5wKUEAkTFt1xG1R+3uPT71BqI3qf0D7fr4qiBtvRhpX0bcdh0x3XslN+yid3XvPmRvnc/yCOzfOlj7ftXTtc897Lgs3+Hs9pfxVgwfKG55WZ6CiTygaiSmnY1tJqGi7eLQAatS2KgCTgrYJ3kF5khJT+JQt7ouZPNKVDCADruESTMxRYRUUsime2nZgVVRFGLpRY+VhOeyrPaGYZ170ntDwVlY4lTIMUJKxu1wZv7g9MNe4sJhdnrvszcKsBJJ7v+2VdutvFkx+b1WV+nbrOC9LVFdimhwZBFSFpou0cWIEz/Q0FOXCFUxDgUfiUlRjRYeqGOpmeOm4Y133uXW7Tu8MNkBv8rg9QS0nDMvvfQym4VzAspzzz7D3tYG84MDZDTm6aef5plnnuHWnTu88/77HJ5Myfe5ZT/1BsIcRwar7MTy6lUoDMtsaGzvMXjnSb1BgbULeg7OAHeHm2fAh36FOPOp0/Fjv+1DZiJWh36wz53d/hRIyenitn44egAwkHHEztSRx6GmnS/RLjEKgdoJE++o1Na0rBk3ZIvsoePsSkwhExXRVtWS9+83EcoK6AYuiysr/+Dql+/Rpz2leCp3hQasz789sI7CPyipvII0r8X55WT11IdPGfiznln/IA6GuO+Cw8rVF8S4DEX0BueKJ6ClMZB5BlJCn76LuPegoraax0SKinoHIdBmZdZFDpcNs9jx4Y2bfHTjJi++8nlTt+7NWkkdhxDY3dlhVDmOjo44OjokNh2TEHju6i7PvfAMP/CDX+ALP/gj3Ni/w3/7q7/KyexNQri3CfjUGwhYdwmNjBK8UHlvMamGkt/uU1oGEJFB1SoBT++LIb5df60AusB6fLa2AYDKkMaz1z45nOFhhmH4OjBPHULlnGELzhNjKTvOmflsTlrMGTvPyHtq76iDFR0levqwDlwQ6DGBTF+6JiXtmLJaG4JS2dmvUuaAlIeDoukoJrZi9GnDN4JzBHzBEfTUxTrlaa397olTOmQ9cjFQhYswIB1DVGGhy9oldAUk6RcFV0KdFTZRzqEP14qBsIp1S7+qQBQdOBcJE5yNoqbUrTanTiil4ZE2RrqcyWKtAfCeJndM245Z20E1oomJ733vDb744z9JqCq0eA2U71F7DxsbhLpCSoFdM5/yyisvsbiyxbPPPMULT19lc2OTnS7x3DPPsn94xMl8fr4bXcYTYSB6J17MhcB5a9Nm3HWP5IyWwhQLbYVc0lfnhhC2UwYTIOUGL66n6iqVdspNOOMy3GVIPsFxNhzpHxxZs3KG4ZVaBC2GNXiqwoiM2plHoMJy1jEW2J6MGKkyDlA7cLmvVOyZCIAISVZZCi0G2oFVTJaH35eiI0q4EUvs3eREcJ6kkPKqtNrsgOETFoUXY1DYkP316XUderVsl0sGQUByebcYc5Chx8VadGTzo+U79H/3cytGbNIUi17F+sQz3ECDspnogKlkbM760MVUrleELkTIfZGaD4XoBG1SqB3qPEmVadOw6DrEe+o60MQl33rta/x7f+Hneea556zupdx5vkgEZjLkyN7WBnsjz0Ze4GcHXHt/iSxPmB3cYL+qUVfx01/5cZ559mm+/b03ef+99y+8z54MA6HJrkMIhKqyCyEMrLi74JY1d/Xc/WGAY49Eu9LzEISYIhRyzeCi61ohj5PVPgS7+dbc0Y8z5XkvYLIfvZEULDUZZMX8tFJiq6EAxbmEegsfiEpd14wks1kJlSZCFiosQ9EhJvtZjtNpxqv5D5GMi4pHTOVaHSMcyfmhJV5EaYqwbVbHpK7QKqDeIZpJ7dK+XxFyMcO1CiWGWo1yfJ8LAInDSSnXVreiQBc+mwhko1gimaEjlyu9MGJx1QUsLJKCmRSzV6kRplwIxJgKTlHOKedBk988ndJXFDek2HNWtIuFQ+FJ3tEpjMVyHTkJXVSoRuRQ0yWYNUuO5o3Nn3fE3OE1c3T7Jvs3bvD8c8+TnSO73iuzGQriqKuApiXN4phxnLHnIouQmbcz8txxeBvEj3j+2Rd56sqXqauaf/lb//rC++mJMBDDkKLFp9m8BrXUWnk86eOygZTCBau7amFclhXXG1A2XOph9e33sXYTrR/qDF7xOMe6N7D++7IGqD9F+xxDOfag4aA9ybiFlAm+YqMKTLxjJHZj9N6AeI+maMBb71rbXkxsJASqYCBhytn0HPqGNyXkF7GHzGQe1AyBd+vUiUEMNmk2zKlchCwrD8OJPZ25xC/ifQE9y7WWs8hLOdP+OMPx+iWgjzN0lYrtoYZT16NfTLRkJZQsvaUooKaC5Gy1FGJegmoa8LOsmZQiJIdWdqCui4ARpZrSDHi+aEi9WE4fuqkyPz7k3Tdf50tf+mIJkQQtoVSvCRpzR24buuWC5fExcTZlc+TZGG9RbY45iks+unGTO4dHXHn6RRbz6T2xryfIQKy+RJ9hGJqlnPqCayzKC/a0Ah512J8r7fz61WoA4CgrdrEFZw3PJ+U1DOdxmc+U37l3Q51VIyYBl42URDGuYISeuqqYjMbUzlGJ4lWppHS8cg6nVneRV441dltSOASmiaDOuk4HyWjbYrbA2Ah9oVVPh3dIedCT6Uc4x/+/vXMLjaMK4/jvn91saxOlifoQrWgKIvTJ1j4YFZHWaxGfFfog6LvVB0nok4+KiIggihfES0VqUQhIFfS5WvEWSWKNsa2itIJob+6u2c+HczaZpjupm7A7J/b7wbAzZ3bIb85MPs6cOWe/cm8ZYoCZf1tRCoGhOZ0aLCTsVYlyuRQGWTUa8RplRsNoYSqVmpGKZhBYaBXGrof5yjunli22YMKJhuTE1pxApvmAYZm7o6f5KDwXOhFLlKDR/Dk7UE8PtVqNer1OaU0F9VY4U69Rr//DXL0eZpOWe5BCs02NBrValcOzM5w+dZJyZW0cgDZHw+bosTDOoVo7g6qnqf9dpVqtzRv1lsv09/VT6V3HyWqDHw8fYfLQT/xx4hSLQ+pZ91LqowAlnQCmi/ZYgsuA34uWWILU/SB9x/+739VmdnmrHauhBTFtZluLlshD0kH3WxmpO17Ifsud7u04zgWABwjHcXJZDQHipaIFzoP7rZzUHS9Yv+Q7KR3HKY7V0IJwHKcgPEA4jpNLsgFC0t2SpiX9IGm0IIerJH0qaVLSd5IeieWDkj6WdCh+DmSOGYvO05Lu6pJnSdKXksYT9Vsvaa+kqViXIyk5Sno0Xt8JSXskrS3aT9Krko5JmsiUte0k6QZJ38Z9z6ndKcOtfgm56IUwZH4G2AhUgK+BTQV4DAFb4vrFwPfAJuApYDSWjwJPxvVN0XUNMBzPodQFz8eAt4HxuJ2a3+vAw3G9AqxPxZGQwGkWuChuvws8WLQfcCuwBZjIlLXtBHwGjBAGv34I3NOWR6dvjmVWzgiwP7M9Bowl4PUBcAdhZOdQLBsiDOY6xxPYD4x02GkDIT/qtkyASMnvkvgPqEXlSTjGAHEUGCQMHBwH7kzBj5AcOxsg2nKK35nKlD8AvNiOQ6qPGM2L1mTJVH3dQCE/6WbgAItSDwLZ1IPd9n4WeJyzc7Cm5LcROA68Fh+DXpbUl4qjmf0CPE1I3/Ar8KeZfZSK3yLadbqSNlJetiLVANHqOamw97GS+oH3gF1m9tdSX21R1jFvSfcCx8zsi/96SIuyTtdrmdBUfsHMNgOniNngc+h2HQ4Qkk4PA1cAfZJ2LnVIi7KixwrkOa3YNdUAkZfCr+tI6iUEh7fMbF8sbjf1YKe4GbhPIXv6O8A2SW8m5Nf8mz+b2YG4vZcQMFJxvB2YNbPjZlYH9gE3JeSXpespL1MNEJ8D10oallQB7iek9esqscf3FWDSzJ7J7Gor9WCn/MxszMw2mNk1hDr6xMx2puIXHX8Djkq6LhZtJ2ReS8XxCHCjpHXxem8HJhPyy9L9lJed7KBaYQfNDsJbgxlgd0EOtxCaZN8AX8VlB3ApoWPwUPwczByzOzpP02aP8Qpdb2OhkzIpP+B64GCsx/eBgZQcgSeAKWACeIPwNqBQP2APoU+kTmgJPLQcJ2BrPK8Z4HkWdRafb/Gh1o7j5JLqI4bjOAngAcJxnFw8QDiOk4sHCMdxcvEA4ThOLh4gHMfJxQOE4zi5/AtzJhCnyrVrkgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为前面已经引入过对应的头文件了，所以不需要再引入一遍了\n",
    "\n",
    "img = cv2.imread(\"../img_use/kauile.jpg\")\n",
    "cv2.imshow(\"test\",img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "imgshow(img)        # 在这个地方，可以看到我们使用了上面的自定义函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个地方要注意到，代码当中imread进行读取的是一个相对路径上的图片，而不是绝对路径上的图片；实际上也可以修改程序绝对路径的图片，因为需要进行实用化，快速部署话，所以这里使用的是相对路径"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.waitKey函数\n",
    "这个函数是在imshow之后所必须要有的函数，其语法格式：\n",
    "```\n",
    "keyvalue = cv2.waitKey(delayTime)\n",
    "```\n",
    "其中的时间的单位为ms，对应的返回值是键盘按下对应的ascii码  \n",
    "* 要注意一下，waitKey如果是给0的话表示的是无限等待，直到一个按键被按下\n",
    "* 比较重要的一点是，在imshow之后是必须要进行waitKey的，因为waitKey对应的时间内是inshow进行绘制图像  \n",
    "  \n",
    "很多的时候，使用waitKey函数来做与用户的交互函数  \n",
    "如下面的例子：按下A键就关闭窗口，按下B键就生成一个窗口副本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow(\"test\",img)\n",
    "key = cv2.waitKey(0)\n",
    "if key is ord('A'):\n",
    "    cv2.destroyAllWindows()\n",
    "elif key is ord('B'):\n",
    "    cv2.imshow(\"New imshow\",img)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为涉及到窗体，所以没有对应的运行显示在jupyter上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.destroyWindow函数\n",
    "该函数的语法格式：\n",
    "```\n",
    "None = cv2.destroyWindows(windowName)\n",
    "```\n",
    "就是他的英语意思，销毁摧毁名字为windowName的窗体，一般来说在程序结束的时候进行窗体的释放  \n",
    "同样的还有一个函数`destroyAllWindows()`:  \n",
    "这个函数是销毁或者摧毁所有的因为这个程序而产生的窗体，个人更简易在程序末使用这个函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.3保存图片\n",
    "OpenCV当中提供了`cv2.imwrite()`函数来进行保存图片，其对应的语法格式：  \n",
    "```\n",
    "flag = cv2.imwrite(filename,img[,params])\n",
    "```\n",
    "* flag是返回值，如果保存成功就返回逻辑真，失败就返回逻辑假\n",
    "* filename 是保存图片的完整的路径名，并且其中是需要写扩展名的\n",
    "* img 是被保存的图像的名称\n",
    "* params  是保存类型参数，是可以选择的\n",
    "\n",
    "注意：\n",
    "* 使用此imwrite只能保存8位单通道或3通道（带有BGR通道顺序）图像  \n",
    "* 对应的例外：\n",
    "  * 对于PNG，JPEG2000和TIFF格式，可以保存16位无符号（CV_16U）图像。\n",
    "  * 32位浮点（CV_32F）图像可以保存为PFM，TIFF，OpenEXR和Radiance HDR格式; 使用LogLuv高动态范围编码（每像素4个字节）保存3通道（CV_32FC3）TIFF图像\n",
    "  * 可以使用此功能保存带有Alpha通道的PNG图像。为此，创建8位（或16位）4通道图像BGRA，其中alpha通道最后。完全透明的像素应该将alpha设置为0，完全不透明的像素应该将alpha设置为255/65535。\n",
    "* 如果格式，深度或通道顺序不同，请在保存之前使用Mat :: convertTo和cv :: cvtColor进行转换。或者，使用通用FileStorage I / O函数将图像保存为XML或YAML格式\n",
    "\n",
    "#### 对应的编码参数\n",
    "|值|解释|\n",
    "|:-------:|:-------:|\n",
    "|cv.IMWRITE_JPEG_QUALITY|对于JPEG，它可以是从0到100的质量（越高越好）。默认值为95|\n",
    "|cv.IMWRITE_JPEG_PROGRESSIVE|启用JPEG功能，0或1，默认为False|\n",
    "|cv.IMWRITE_JPEG_OPTIMIZE|启用JPEG功能，0或1，默认为False|\n",
    "|cv.IMWRITE_JPEG_RST_INTERVAL|JPEG重启间隔，0 - 65535，默认为0 - 无重启|\n",
    "|cv.IMWRITE_JPEG_LUMA_QUALITY|单独的亮度质量等级，0 - 100，默认为0 - 不使用|\n",
    "|cv.IMWRITE_JPEG_CHROMA_QUALITY|单独的色度质量等级，0 - 100，默认为0 - 不使用|\n",
    "|cv.IMWRITE_PNG_COMPRESSION|对于PNG，它可以是从0到9的压缩级别。值越高意味着更小的尺寸和更长的压缩时间。如果指定，则策略更改为IMWRITE_PNG_STRATEGY_DEFAULT（Z_DEFAULT_STRATEGY）。默认值为1（最佳速度设置）|\n",
    "|cv.IMWRITE_PNG_STRATEGY|默认为IMWRITE_PNG_STRATEGY_RLE|\n",
    "|cv.IMWRITE_PNG_BILEVEL|二进制级别PNG，0或1，默认为0|\n",
    "|cv.IMWRITE_PXM_BINARY|对于PPM，PGM或PBM，它可以是二进制格式标志，0或1.默认值为1|\n",
    "|cv.IMWRITE_EXR_TYPE||\n",
    "|cv.IMWRITE_WEBP_QUALITY|覆盖EXR存储类型（默认为FLOAT（FP32））\n",
    "对于WEBP，它可以是1到100的质量（越高越好）。默认情况下（不带任何参数），如果质量高于100，则使用无损压缩|\n",
    "|cv.IMWRITE_PAM_TUPLETYPE|对于PAM，将TUPLETYPE字段设置为为格式定义的相应字符串值|\n",
    "|cv.IMWRITE_TIFF_RESUNIT|对于TIFF，用于指定要设置的DPI分辨率单位; 请参阅libtiff文档以获取有效值|\n",
    "|cv.IMWRITE_TIFF_XDPI|对于TIFF，用于指定X方向DPI|\n",
    "|cv.IMWRITE_TIFF_YDPI|对于TIFF，用于指定Y方向DPI|\n",
    "|cv.IMWRITE_TIFF_COMPRESSION|对于TIFF，用于指定图像压缩方案。请参阅libtiff以获取与压缩格式对应的整数常量。注意，对于深度为CV_32F的图像，仅使用libtiff的SGILOG压缩方案。对于其他支持的深度，可以通过此标志指定压缩方案; LZW压缩是默认值|\n",
    "|cv.IMWRITE_JPEG2000_COMPRESSION_X1000|对于JPEG2000，用于指定目标压缩率（乘以1000）。该值可以是0到1000.默认值是1000|\n",
    "\n",
    "注意，如果是直接保存的话，是保存到和代码一个路径下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "flas = cv2.imwrite(\"img.jpg\",img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "是可以在同一级的目录下看到对应的保存的图片的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 OpenCV 库模块介绍\n",
    "OpenCV的库包含了两个部分，一个是主库，一个是contrib库  \n",
    "* 主库是比较稳定且成熟的，核心由OpenCV团队维护  \n",
    "* contirb库，主要由社区开发和维护，其中包含的算法更加全面，并且包含了一些受专利保护的算法，使用之前要注意是否商业化\n",
    "\n",
    "OpenCV库模块介绍：\n",
    "* calib3d——这个模块的名称是由calibration（校准）和3D两个属于的缩写组合而成的，该模块主要包含相机标定与立体视觉等功能，例如物体位姿估计、三维重建、摄像头标定等\n",
    "* core——核心功能模块。这个模块主要包含OpenCV库的基础结构以及基本操作，例如OpenCV基本数据结构、绘图函数、数组操作等相关函数\n",
    "* dnn——深度学习模块。这个模块是OpenCV4版本的特色，主要包括了构件神经网络加载序列化网络模型。但是这个模块模块仅适用于正向传递计算，原则上不支持反向计算\n",
    "* features2d——这个模块名称是由features（特征）和2D两个术语的缩写组合而成，功能主要为处理图像特征，例如特征检测、描述与匹配\n",
    "* flann——这个模块的全程是快速近似最近邻库的缩写。这个模块是高维的近似邻快速的搜索算法库，主要包含快速近似近邻搜索与聚类等\n",
    "* gapi——这个模块是OpenCV4中新添加的模块，旨在加速常规的图像处理。与其他模块相比，这个模块主要充当框架，而不是某些特定的计算机视觉算法\n",
    "* highgui——高层GUI，包含创建和操作显示图像、处理鼠标事件以及键盘命令、提供图像交互可视化界面等\n",
    "* imgcodecs——图像文件读取与保存模块，主要用于图像文件读取与保存\n",
    "* imgproc——这个模块名称是由（图像）和process（处理）两个术语组成，是重要的图像处理模块，包含了图像滤波、几何变换、直方图、特征检测与目标检测等\n",
    "* ml——机器学习模块，主要为统计分类、回归和数据聚类等\n",
    "* objdetect——目标检测模块，主要用于图像目标检测，例如检测Harr特征\n",
    "* photo——计算摄影模块，主要包含图像修复和去噪等算法\n",
    "* stitching——图像拼接模块，主要包含了特征点的寻找与匹配图像、估计旋转、自动校准、接缝估计等图像拼接过程的相关内容\n",
    "* video——视频分析模块，主要包含了运动估计、背景分离、对象跟踪等视频处理相关内容\n",
    "* videoio——视频输入/输出模块，主要用于读取/写入视频"
   ]
  }
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